Image-Text-to-Text
Transformers
Safetensors
qwen3_5
ocr
document-parsing
multimodal
markdown
tables
formulas
key-information-extraction
figure-captioning
vllm
conversational
Instructions to use sekkit/OvisOCR3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sekkit/OvisOCR3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="sekkit/OvisOCR3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("sekkit/OvisOCR3") model = AutoModelForMultimodalLM.from_pretrained("sekkit/OvisOCR3", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sekkit/OvisOCR3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sekkit/OvisOCR3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sekkit/OvisOCR3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/sekkit/OvisOCR3
- SGLang
How to use sekkit/OvisOCR3 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "sekkit/OvisOCR3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sekkit/OvisOCR3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "sekkit/OvisOCR3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sekkit/OvisOCR3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use sekkit/OvisOCR3 with Docker Model Runner:
docker model run hf.co/sekkit/OvisOCR3
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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 | """Single-file OvisOCR3 document parsing and cross-page table reconstruction.
Reading guide: public entry point -> document pipeline -> text/table helpers ->
model backend -> browser renderer. Import the text helpers without installing
vLLM or browser dependencies; those are loaded only when used.
Algorithm: parse pages independently, then visit adjacent table-bearing pages
from last to first. Write each merged table into the earlier page and render
that updated page before its next prediction.
Keep non-table source untouched.
Support: the document pipeline accepts one merged table per adjacent pair and
binds the earlier page's last table to the later page's first table. Multiple
merges need explicit fragment bindings via apply_pair(); nested or incomplete
HTML tables are rejected on writeback. This is a conservative binding policy,
not a general table-fragment matching algorithm.
Page numbers are 1-based; table fragment indices are 0-based. Model predictions
use pair-local pages 1 and 2; saved Markdown and provenance use document pages.
Raw predictions, intermediate files and partial results are retained.
"""
from __future__ import annotations
import argparse
import base64
import copy
import html
import io
import json
import math
import re
import time
import traceback
import uuid
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Callable, Mapping, NamedTuple
NEGATIVE = "No cross-page table exists between these pages."
TABLE = re.compile(r"<table\b[^>]*>.*?</table\s*>", re.I | re.S)
IMAGE_REF = re.compile(
r"(?<![\w/])(?:page_(\d+)/)?images/bbox_(\d+)_(\d+)_(\d+)_(\d+)\.jpg"
)
REF = re.compile(r"page_(\d+)/images/")
CATEGORIES = set(
"abstract algorithm aside_text chart content display_formula doc_title figure_title footer footer_image footnote formula_number header header_image image inline_formula number paragraph_title reference_content seal table text vertical_text vision_footnote".split()
)
FragmentId = tuple[int, int]
Backend = Callable[[list[str], str, Path, int], tuple[str, str]]
class GenerationResult(NamedTuple):
"""Tuple-compatible raw model output; cleaning belongs to the page adapter."""
text: str
finish_reason: str
class RenderResult(NamedTuple):
"""Tuple-compatible render result; aliases map rendered crops to originals."""
image_path: str
rewritten_source: str
alias_to_original: dict[str, str]
diagnostics: dict
@dataclass
class PageState:
"""One document page and the original fragments behind its current tables."""
number: int
image_path: str
original_markdown: str
markdown: str
finish_reason: str
parse_diagnostics: dict | None = None
table_sources: list[set[FragmentId]] = field(init=False)
def __post_init__(self):
self.table_sources = [
{(self.number, index)}
for index, _ in enumerate(TABLE.finditer(self.markdown))
]
@dataclass
class PairInput:
"""Two model inputs with explicit document/pair-local coordinate mapping."""
page_numbers: tuple[int, int]
image_paths: list[str]
markdowns: list[str]
alias_to_original: dict[str, str]
rendered_pages: set[int]
@dataclass
class DocumentState:
pages: list[PageState] = field(default_factory=list)
chunks: list[list[int]] = field(default_factory=list)
steps: list[dict] = field(default_factory=list)
merged_tables: list[dict] = field(default_factory=list)
parse_warnings: list[dict] = field(default_factory=list)
# Public entry point and document pipeline.
def reconstruct_document(
image_paths,
output,
*,
model_path=".",
stage1_prompt="document_structured",
mathjax=None,
backend: Backend | None = None,
renderer=None,
):
"""Reconstruct ordered page images into a document, keeping all evidence.
Use a fresh output directory. Stage 1 accepts document_markdown[.txt] or
document_structured[.txt]; prompts are loaded from model_path/prompts.
Returns the saved result.json dictionary. Truncated generations, recovered
JSON or invalid structured fields return PARTIAL with issue-specific warnings;
other processing failures save partial output and raise RuntimeError.
For research, inject backend(paths, prompt, folder, stage) -> (raw, reason)
and/or renderer.render(source, owner, paths, folder, name) -> RenderResult.
Injected renderers remain caller-owned. Defaults load vLLM and Chromium lazily.
Engine settings, prompt bytes and renderer resize policy are unchanged.
"""
stage1_prompt = stage1_prompt.removesuffix(".txt")
if stage1_prompt not in {"document_markdown", "document_structured"}:
raise ValueError("Choose document_markdown or document_structured")
images = _document_images(image_paths)
if Path(output).exists():
raise FileExistsError("Use a fresh output directory")
prompt_dir = Path(model_path) / "prompts"
page_prompt = (prompt_dir / f"{stage1_prompt}.txt").read_text().removesuffix("\n")
table_prompt = (prompt_dir / "crosspage_table.txt").read_text().removesuffix("\n")
if backend is None:
backend = VllmBackend(model_path)
owns_renderer = renderer is None
if owns_renderer:
renderer = _BrowserRenderer(
mathjax or Path(__file__).resolve().parent / "assets/mathjax.js"
)
try:
result = _run_document(
images,
output,
backend,
renderer,
page_prompt=page_prompt,
table_prompt=table_prompt,
stage1_prompt=stage1_prompt,
)
finally:
if owns_renderer:
renderer.close()
if result["error"] and not result["error"].get("recoverable", False):
raise RuntimeError(f"Document incomplete; inspect {output}/failure.json")
return result
def _run_document(
image_paths, output, backend, renderer, *, page_prompt, table_prompt, stage1_prompt
):
"""Parse pages, reconstruct adjacent pairs backwards, then persist results.
Recovery is confined to Stage 1 output parsing. A failed window stops further
reconstruction while preserving earlier pages, merges and failure details.
No benchmark labels are used. The output directory must not already exist.
"""
images = _document_images(image_paths)
output = Path(output)
output.mkdir(parents=True, exist_ok=False)
save(
output / "inputs.json",
{
"images": images,
"stage1_prompt": stage1_prompt,
"page_prompt": page_prompt,
"table_prompt": table_prompt,
},
)
state = DocumentState()
error = None
context = {"stage": 1, "pages": []}
try:
for number, image in enumerate(images, 1):
context = {"stage": 1, "pages": [number]}
page = _parse_page(
number, image, output, backend, page_prompt, stage1_prompt
)
state.pages.append(page)
warning = _page_warning(page)
if warning:
state.parse_warnings.append(warning)
state.chunks = find_chunks([page.markdown for page in state.pages])
save(
output / "chunks.json",
{
"chunks": state.chunks,
"basis": "Stage 1 predicted HTML tables only",
},
)
for left, right in _adjacent_pairs(state.chunks):
context = {"stage": 2, "pages": [left, right]}
folder = output / "stage2" / f"pair_{left:04d}_{right:04d}"
folder.mkdir(parents=True)
pair = _prepare_pair(
state.pages[left - 1 : right], images, folder, renderer
)
tables = _predict_pair(pair, folder, backend, table_prompt)
_apply_merge(state, pair, tables)
state.steps.append(
{
"pages": list(pair.page_numbers),
"merged": bool(tables),
"rendered_pages": sorted(pair.rendered_pages),
}
)
save(
folder / "result.json",
{
"tables": tables,
"pages": [
state.pages[number - 1].markdown for number in pair.page_numbers
],
"groups": copy.deepcopy(state.merged_tables),
},
)
except Exception as exc:
error = {
"type": type(exc).__name__,
"message": str(exc),
"recoverable": False,
**context,
}
save(output / "failure.json", {**error, "traceback": traceback.format_exc()})
return _finalize_document(state, images, output, error)
def _parse_page(number, image, output, backend, page_prompt, stage1_prompt):
"""Retain raw output, apply the existing cleanup, then adapt the page format."""
folder = output / "stage1" / f"page_{number:04d}"
folder.mkdir(parents=True)
generation = GenerationResult(*backend([image], page_prompt, folder, 1))
raw, reason = generation
(folder / "raw.txt").write_text(raw)
cleaned = _clean_truncated_repeats(raw.strip())
save(
folder / "generation.json",
{
"finish_reason": reason,
"complete": reason == "stop",
"tail_changed": cleaned != raw.strip(),
"raw_chars": len(raw.strip()),
"cleaned_chars": len(cleaned),
},
)
(folder / "cleaned_raw.txt").write_text(cleaned)
if reason not in ("stop", "length"):
raise ValueError(f"Stage 1 page {number}: finish_reason={reason}")
diagnostics = None
if stage1_prompt == "document_structured":
projected, blocks, diagnostics = structured_to_markdown(cleaned)
save(folder / "blocks.json", blocks)
save(folder / "adapter.json", diagnostics)
(folder / "projected.md").write_text(projected)
markdown = page_markdown(projected, number) if projected.strip() else ""
else:
markdown = page_markdown(cleaned, number)
(folder / "page.md").write_text(markdown)
return PageState(number, image, markdown, markdown, reason, diagnostics)
def _page_warning(page):
"""Separate generation truncation, JSON recovery and structured schema issues."""
codes = []
if page.finish_reason == "length":
codes.append("generation_truncated")
diagnostics = page.parse_diagnostics
if diagnostics is not None:
if not diagnostics["json_complete"]:
codes.append("json_incomplete")
if diagnostics["schema_issues"]:
codes.append("schema_invalid")
if (
diagnostics["invalid_content_objects"]
or not diagnostics["emitted_contents"]
):
codes.append("content_invalid")
if not codes:
return None
warning = {"page": page.number, "finish_reason": page.finish_reason, "codes": codes}
if diagnostics is not None:
warning["diagnostics"] = diagnostics
return warning
def _adjacent_pairs(chunks):
"""Yield (2, 3), then (1, 2) for a three-page table-bearing run."""
for chunk in chunks:
for left in range(chunk[-1] - 1, chunk[0] - 1, -1):
yield left, left + 1
def _prepare_pair(pages, images, folder, renderer):
page_numbers = tuple(page.number for page in pages)
document_to_local = {number: local for local, number in enumerate(page_numbers, 1)}
paths, markdowns, aliases, rendered_pages = [], [], {}, set()
for page in pages:
path, markdown = page.image_path, page.markdown
if markdown != page.original_markdown:
rendered = RenderResult(
*renderer.render(
markdown, page.number, images, folder, f"page_{page.number:04d}"
)
)
path, markdown = rendered.image_path, rendered.rewritten_source
aliases.update(rendered.alias_to_original)
rendered_pages.add(page.number)
paths.append(path)
markdowns.append(remap(markdown, document_to_local))
return PairInput(page_numbers, paths, markdowns, aliases, rendered_pages)
def _predict_pair(pair, folder, backend, table_prompt):
page_context = "\n\n".join(
f'<INPUT_PAGE index="{index}">\n{markdown}\n</INPUT_PAGE>'
for index, markdown in enumerate(pair.markdowns, 1)
)
prompt = table_prompt.removesuffix("\n") + "\n\nInput Markdown:\n\n" + page_context
if all(TABLE.search(markdown) for markdown in pair.markdowns):
generation = GenerationResult(*backend(pair.image_paths, prompt, folder, 2))
else:
generation = GenerationResult(
json.dumps([{"source_pages": [], "html": NEGATIVE}]), "stop"
)
save(
folder / "gate_skip.json",
{"reason": "README table gate", "model_called": False},
)
(folder / "raw.txt").write_text(generation.text)
save(folder / "generation.json", {"finish_reason": generation.finish_reason})
if generation.finish_reason != "stop":
raise ValueError(
f"Stage 2 pair {list(pair.page_numbers)}: finish_reason={generation.finish_reason}"
)
tables = parse_tables(generation.text)
if len(tables) > 1:
raise ValueError(
"Multiple merges need explicit fragment bindings; output retained."
)
return [
dict(table, html=_restore_image_refs(table["html"], pair)) for table in tables
]
def _restore_image_refs(table_html, pair):
"""Restore document page IDs, then resolve crops from re-rendered pages exactly."""
local_to_document = {
local: number for local, number in enumerate(pair.page_numbers, 1)
}
table_html = remap(table_html, local_to_document)
def restore(match):
reference = match[0]
if match[1] is None:
raise ValueError("Output crop lacks a page number.")
if int(match[1]) in pair.rendered_pages:
if reference not in pair.alias_to_original:
raise ValueError("Unmappable crop from a rendered page: " + reference)
return pair.alias_to_original[reference]
return reference
return IMAGE_REF.sub(restore, table_html)
def _apply_merge(state, pair, tables):
"""Validate writeback before updating page contents or fragment provenance."""
earlier, later = (state.pages[number - 1] for number in pair.page_numbers)
before = (earlier.markdown, later.markdown)
after = apply_pair(before, tables)
if any(TABLE.sub("", old) != TABLE.sub("", new) for old, new in zip(before, after)):
raise ValueError("Writeback changed non-table content.")
if tables:
sources = earlier.table_sources[-1] | later.table_sources[0]
state.merged_tables = [
group
for group in state.merged_tables
if not sources.intersection(map(tuple, group["fragment_ids"]))
]
state.merged_tables.append(
{
"source_pages": sorted({number for number, _ in sources}),
"fragment_ids": sorted(sources),
"html": tables[0]["html"],
}
)
earlier.table_sources[-1] = sources
del later.table_sources[0]
earlier.markdown, later.markdown = after
def _save_document_crops(markdown, images, output):
"""Materialize page-qualified references from the original input images."""
from PIL import Image
errors = []
references = {}
for match in IMAGE_REF.finditer(markdown):
references.setdefault(match[1], {})[match[0]] = match.groups()[1:]
for page, crops in references.items():
try:
if page is None or not 1 <= int(page) <= len(images):
raise ValueError("Image reference needs a valid source page number")
with Image.open(images[int(page) - 1]) as source:
for reference, coordinates in crops.items():
try:
left, top, right, bottom = map(int, coordinates)
width, height = source.size
box = (
max(0, min(width, round(left * width / 1000))),
max(0, min(height, round(top * height / 1000))),
max(0, min(width, round(right * width / 1000))),
max(0, min(height, round(bottom * height / 1000))),
)
if box[2] <= box[0] or box[3] <= box[1]:
raise ValueError("Image crop has zero or negative pixel area")
target = output / reference
target.parent.mkdir(parents=True, exist_ok=True)
source.crop(box).convert("RGB").save(target)
except Exception as exc:
errors.append({"reference": reference, "message": str(exc)})
except Exception as exc:
errors.extend(
{"reference": reference, "message": str(exc)} for reference in crops
)
return errors
def _finalize_document(state, images, output, error):
document = "\n\n".join(page.markdown for page in state.pages) + "\n"
crop_errors = _save_document_crops(document, images, output)
for page in state.pages:
target = output / "pages" / f"page_{page.number:04d}.md"
target.parent.mkdir(exist_ok=True)
# Per-page Markdown is one directory below the shared crop assets.
target.write_text(IMAGE_REF.sub(lambda m: "../" + m[0], page.markdown))
(output / "document.md").write_text(document)
if crop_errors and error is None:
error = {"type": "ImageCropExportFailed", "recoverable": False}
save(output / "failure.json", {**error, "image_export_errors": crop_errors})
if error is None and state.parse_warnings:
error = {
"type": "Stage1OutputIncomplete",
"recoverable": True,
"pages": [warning["page"] for warning in state.parse_warnings],
}
result = {
"status": "COMPLETE" if error is None else "PARTIAL",
"error": error,
"input_pages": len(images),
"parsed_pages": len(state.pages),
"chunks": state.chunks,
"steps": state.steps,
"merged_tables": state.merged_tables,
"stage1_parse_warnings": state.parse_warnings,
"image_export_errors": crop_errors,
}
save(output / "result.json", result)
return result
# Reusable text adapters and table operations (standard library only).
def _document_images(image_paths):
images = [str(Path(path).resolve()) for path in image_paths]
if not images or not all(Path(path).is_file() for path in images):
raise ValueError("Provide existing page images in reading order.")
return images
def save(path, value):
"""Write a JSON artifact, creating its parent directory when needed."""
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(value, ensure_ascii=False, indent=2) + "\n")
def _is_valid_bbox(value):
if not isinstance(value, list) or len(value) != 4:
return False
if not all(type(coordinate) is int for coordinate in value):
return False
left, top, right, bottom = value
return 0 <= left <= right < 1000 and 0 <= top <= bottom < 1000
def _recover_json_prefix(text):
"""Return only complete prefix objects; never guess or repair JSON syntax."""
blocks, unparsed_tail_chars = [], 0
if not text.startswith("["):
return blocks, unparsed_tail_chars
position = 1
decoder = json.JSONDecoder()
while position < len(text):
while position < len(text) and text[position].isspace():
position += 1
if position == len(text) or text[position] == "]":
break
try:
block, end = decoder.raw_decode(text, position)
except json.JSONDecodeError:
unparsed_tail_chars = len(text) - position
break
blocks.append(block)
position = end
while position < len(text) and text[position].isspace():
position += 1
if position < len(text) and text[position] == ",":
position += 1
elif position < len(text) and text[position] != "]":
unparsed_tail_chars = len(text) - position
break
return blocks, unparsed_tail_chars
def structured_to_markdown(text):
"""Project content in emitted order, retaining diagnostics for incomplete JSON.
Preserve every category and keep only complete prefix objects on JSON failure.
Invalid bbox/category fields are reported without discarding valid content.
"""
stripped = text.strip()
if stripped.startswith("```") and stripped.endswith("```"):
stripped = re.sub(r"^```(?:json)?\s*\n|\n```$", "", stripped, flags=re.I)
diagnostics = {
"json_complete": False,
"schema_issues": [],
"invalid_content_objects": [],
"unparsed_tail_chars": 0,
}
try:
blocks = json.loads(stripped)
if not isinstance(blocks, list):
raise ValueError("Expected one JSON array")
diagnostics["json_complete"] = True
except ValueError as exc:
diagnostics["parse_error"] = str(exc)
blocks, diagnostics["unparsed_tail_chars"] = _recover_json_prefix(stripped)
contents = []
for index, block in enumerate(blocks):
if not isinstance(block, dict) or not isinstance(block.get("content"), str):
diagnostics["invalid_content_objects"].append(index)
continue
contents.append(block["content"])
if not _is_valid_bbox(block.get("bbox")):
diagnostics["schema_issues"].append({"index": index, "field": "bbox"})
if (
not isinstance(block.get("category"), str)
or block["category"] not in CATEGORIES
):
diagnostics["schema_issues"].append({"index": index, "field": "category"})
diagnostics.update(
parsed_objects=len(blocks),
emitted_contents=len(contents),
policy="join content in emitted array order; preserve every category; retain complete prefix objects only",
)
diagnostics["valid"] = (
diagnostics["json_complete"]
and not diagnostics["schema_issues"]
and not diagnostics["invalid_content_objects"]
and bool(contents)
)
return "\n\n".join(contents), blocks, diagnostics
def _clean_truncated_repeats(
text: str,
min_text_len: int = 8000,
max_period: int = 200,
min_period: int = 1,
min_repeat_chars: int = 100,
min_repeat_times: int = 5,
) -> str:
n = len(text)
if n < min_text_len:
return text
max_period = min(max_period, n - 1)
for unit_len in range(min_period, max_period + 1):
if text[n - 1] != text[n - 1 - unit_len]:
continue
match_len = 1
idx = n - 2
while idx >= unit_len and text[idx] == text[idx - unit_len]:
match_len += 1
idx -= 1
total_len = match_len + unit_len
repeat_times = total_len // unit_len
tail_len = total_len % unit_len
if repeat_times >= min_repeat_times and total_len >= min_repeat_chars:
return text[: n - total_len + unit_len] + text[n - tail_len :]
return text
def parse_tables(raw: str) -> list[dict]:
"""Return all positive objects or [] for the exact negative sentinel."""
items = json.loads(raw)
if not isinstance(items, list) or not items:
raise ValueError("Expected a nonempty JSON array.")
for item in items:
if not isinstance(item, dict) or set(item) != {"source_pages", "html"}:
raise ValueError("Each object needs exactly source_pages and html.")
if not isinstance(item["source_pages"], list) or not isinstance(
item["html"], str
):
raise ValueError("Invalid field types.")
if not item["source_pages"]:
if len(items) == 1 and item["html"] == NEGATIVE:
return []
raise ValueError("Invalid or mixed negative sentinel.")
if item["source_pages"] != [1, 2] or any(
type(p) is not int for p in item["source_pages"]
):
raise ValueError("A positive two-page result must use [1, 2].")
if not TABLE.fullmatch(item["html"].strip()):
raise ValueError("Expected complete HTML table output.")
return items
def apply_pair(
markdowns: tuple[str, str],
tables: list[dict],
bindings: list[tuple[int, int]] | None = None,
) -> tuple[str, str]:
"""Replace page 1's last table and remove page 2's first table by default.
Multiple merged tables require explicit zero-based fragment bindings.
Nested or incomplete HTML tables are rejected; non-table source is retained.
"""
if len(markdowns) != 2:
raise ValueError("Need exactly two pages.")
spans = [list(TABLE.finditer(md)) for md in markdowns]
if bindings is None:
if not tables:
return tuple(markdowns)
if len(tables) != 1:
raise ValueError("Multiple merged tables require explicit source bindings.")
bindings = [(len(spans[0]) - 1, 0)]
if len(tables) != len(bindings):
raise ValueError("Need one explicit binding per merged table.")
for md, matches in zip(markdowns, spans):
if len(re.findall(r"<table\b", md, re.I)) != len(matches) or len(
re.findall(r"</table\s*>", md, re.I)
) != len(matches):
raise ValueError(
"Nested or incomplete tables require a layout-aware editor."
)
edits = [{}, {}]
for item, binding in zip(tables, bindings):
parse_tables(json.dumps([item]))
if len(re.findall(r"<table\b", item["html"], re.I)) != 1:
raise ValueError(
"Nested or multiple tables cannot be written into one fragment."
)
if item["source_pages"] != [1, 2] or len(binding) != 2:
raise ValueError("Only positive two-page tables can be written back.")
for page, index in enumerate(binding):
if type(index) is not int or not 0 <= index < len(spans[page]):
raise ValueError("Source table index out of range.")
if index in edits[page]:
raise ValueError("Two results claim the same source fragment.")
edits[page][index] = item["html"] if page == 0 else ""
output = []
for page, md in enumerate(markdowns):
for index in sorted(edits[page], reverse=True):
span = spans[page][index]
md = md[: span.start()] + edits[page][index] + md[span.end() :]
output.append(md)
return tuple(output)
def find_chunks(pages):
"""Maximal runs of >=2 pages with predicted HTML tables; 1-based IDs."""
chunks, run = [], []
for page, markdown in enumerate(pages, 1):
if TABLE.search(markdown):
run.append(page)
else:
if len(run) >= 2:
chunks.append(run)
run = []
if len(run) >= 2:
chunks.append(run)
return chunks
def remap(markdown: str, mapping: Mapping[int, int]) -> str:
def replace(match):
page = int(match[1])
if page not in mapping:
raise ValueError(f"Image page {page} is outside the current pair.")
return f"page_{mapping[page]}/images/"
return REF.sub(replace, markdown)
def page_markdown(raw, page):
"""Accept Markdown, optionally fenced; qualify page-local crop references."""
markdown = re.sub(
r"^```(?:markdown|html)?\s*\n|\n```\s*$", "", raw.strip(), flags=re.I
)
if not markdown:
raise ValueError("Empty Stage 1 Markdown.")
# Each Stage 1 call contains one image, so crop IDs refer to this page.
return IMAGE_REF.sub(
lambda m: f"page_{page}/images/bbox_" + "_".join(m.groups()[1:]) + ".jpg",
markdown,
)
# Model inference and lazy browser lifecycle.
class VllmBackend:
"""Keep engine, sampling and request settings aligned with README infer().
Benchmark-specific scheduling overrides do not belong in this backend.
"""
def __init__(self, model_path):
from vllm import LLM, SamplingParams
self.model = LLM(
model=str(Path(model_path).resolve()),
tensor_parallel_size=1,
gpu_memory_utilization=0.8,
gdn_prefill_backend="triton",
limit_mm_per_prompt={"image": 2},
)
self.tokenizer = self.model.get_tokenizer()
self.sampling = SamplingParams(max_tokens=16384, temperature=0.0)
def __call__(self, paths, task_prompt, folder, stage):
from PIL import Image
images = []
for path in paths:
with Image.open(path) as image:
images.append(image.convert("RGB"))
content = [{"type": "image"} for _ in images]
content.append({"type": "text", "text": task_prompt})
prompt = self.tokenizer.apply_chat_template(
[{"role": "user", "content": content}],
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
request = {
"prompt": prompt,
"multi_modal_data": {"image": images},
"mm_processor_kwargs": {
"images_kwargs": {
"min_pixels": 448 * 448,
"max_pixels": 2880 * 2880,
}
},
}
save(
folder / "input.json",
{
"stage": stage,
"images": list(paths),
"prompt": prompt,
"mm_processor_kwargs": request["mm_processor_kwargs"],
},
)
output = self.model.generate([request], self.sampling)[0].outputs[0]
return GenerationResult(output.text, output.finish_reason)
class _BrowserRenderer:
def __init__(self, mathjax):
self.mathjax = mathjax
self.playwright = self.browser = self.renderer = None
def render(self, *args):
if self.renderer is None:
from playwright.sync_api import sync_playwright
self.playwright = sync_playwright().start()
self.browser = self.playwright.chromium.launch(headless=True)
self.renderer = Renderer(self.browser, self.mathjax)
return self.renderer.render(*args)
def close(self):
if self.browser:
self.browser.close()
if self.playwright:
self.playwright.stop()
# Content-preserving Markdown/HTML rendering.
CROP = re.compile(r"page_(\d+)/images/bbox_(\d+)_(\d+)_(\d+)_(\d+)\.jpg")
_CROP_ASSET = re.compile(
r"(?:page_(\d+)/)?images/(?:bbox_)?(\d+)_(\d+)_(\d+)_(\d+)\.(?:jpg|jpeg|png)", re.I
)
_TAG = re.compile(
r"<!--.*?-->|</?[A-Za-z][\w:-]*(?:[^>\'\"]|\"[^\"]*\"|\'[^\']*\')*>", re.S
)
_SRC = re.compile(r"""(?<![\w:-])src\s*=\s*(?:"([^"]*)"|'([^']*)'|([^\s>]+))""", re.I)
_ALLOWED = set(
"p div span br hr h1 h2 h3 h4 h5 h6 table thead tbody tfoot tr td th caption colgroup col ul ol li dl dt dd blockquote pre code b strong em i u s del sup sub a img figure figcaption section article main center font".split()
)
_DROP = set("script style iframe object embed link meta base template".split())
_CSS = """
html,body{margin:0;padding:0;background:#fff;color:#111}
#capture{padding:28px;box-sizing:border-box;font-family:Arial,'Noto Sans CJK SC',sans-serif;font-size:18px;line-height:1.4}
table{border-collapse:collapse;width:100%;margin:12px 0}td,th{border:1px solid #666;padding:5px;vertical-align:top;overflow-wrap:anywhere}
img{max-width:100%;height:auto}h1{font-size:28px}h2{font-size:24px}h3{font-size:21px}p{margin:10px 0}
pre{white-space:pre-wrap;overflow-wrap:anywhere;font:inherit}code{font-family:monospace;white-space:pre-wrap;overflow-wrap:anywhere}
.math-display{display:block;text-align:center;margin:10px 0}.math-inline{display:inline-block;vertical-align:middle}.math-fallback{white-space:pre-wrap;overflow-wrap:anywhere}
mjx-container{max-width:none}svg{overflow:visible}th{font-weight:600}
"""
@dataclass(frozen=True)
class RenderLimits:
"""Configurable engineering limits, not validated OCR accuracy thresholds."""
max_side: int = 32768
max_render_pixels: int = 64_000_000
max_source_chars: int = 4_000_000
min_effective_font_px: float = 12.0
min_effective_cell_height_px: float = 8.0
timeout_ms: int = 30000
def __post_init__(self):
for name, value in asdict(self).items():
if not math.isfinite(value) or value <= 0:
raise ValueError(name + " must be finite and positive")
class RenderError(ValueError):
"""Concise caller-facing error; details remain in the render.json sidecar."""
def __init__(self, code, message):
self.code = code
super().__init__(message)
def _escaped(text, pos):
start = pos
while start and text[start - 1] == "\\":
start -= 1
return (pos - start) % 2 == 1
def _dollar_is_math(body, mode):
if mode == "all":
return True
if mode == "off" or not body or body != body.strip():
return False
if re.search(r"\\[A-Za-z]+", body):
return True
# Pure numbers and prose are ambiguous; preserve them, rather than invent math.
if re.fullmatch(r"[A-Za-z]", body):
return True
if re.search(r"[^\x00-\x7f]", body):
return bool(
re.fullmatch(r"[\w\s\d.,_^=+*/<>{}()−-]+", body)
and re.search(r"[_^=+*/<>−-]", body)
)
if re.fullmatch(r"\d+(?:\.\d+)?\\[,;!: ](?:[A-Za-z]+)", body):
return True
words = re.findall(r"[A-Za-z]+", body)
if any(
len(w) > 1
and w not in {"sin", "cos", "tan", "log", "ln", "exp", "max", "min", "lim"}
for w in words
):
return False
return bool(re.search(r"[_^=+*/<>{}]", body) or re.search(r"\w\s*-\s*\w", body))
def _markdown_image(text, pos, parser, env):
from markdown_it.common.utils import normalizeReference
from markdown_it.helpers import parseLinkDestination, parseLinkLabel, parseLinkTitle
from markdown_it.rules_inline.state_inline import StateInline
state = StateInline(text, parser, env, [])
end_label = parseLinkLabel(state, pos + 1, False)
if end_label < 0:
return None
alt = text[pos + 2 : end_label]
cursor = end_label + 1
title = ""
if cursor < len(text) and text[cursor] == "(":
cursor += 1
while cursor < len(text) and text[cursor].isspace():
cursor += 1
dest = parseLinkDestination(text, cursor, len(text))
if not dest.ok:
return None
ref, cursor = dest.str, dest.pos
spaces = cursor
while cursor < len(text) and text[cursor].isspace():
cursor += 1
if cursor > spaces:
parsed_title = parseLinkTitle(text, cursor, len(text))
if parsed_title.ok:
title, cursor = parsed_title.str, parsed_title.pos
while cursor < len(text) and text[cursor].isspace():
cursor += 1
if cursor >= len(text) or text[cursor] != ")":
return None
end = cursor + 1
else:
label, end = alt, cursor
if cursor < len(text) and text[cursor] == "[":
close = text.find("]", cursor + 1)
if close < 0:
return None
label = text[cursor + 1 : close] or alt
end = close + 1
definition = env.get("references", {}).get(normalizeReference(label))
if not definition:
return None
ref, title = definition["href"], definition.get("title", "")
return end, ref, alt, title
def _image_markup(item, ref):
if item["kind"] == "html":
return html.escape(ref, quote=True)
title = (
(' "' + item["title"].replace("\\", "\\\\").replace('"', '\\"') + '"')
if item["title"]
else ""
)
return "![" + item["alt"] + "](<" + ref + ">" + title + ")"
def _edit_source(text, edits):
out, previous = [], 0
for start, end, value in sorted(edits):
if start < previous:
raise RenderError(
"overlapping_source_spans",
"Cannot safely map overlapping source elements.",
)
out.extend([text[previous:start], value])
previous = end
out.append(text[previous:])
return "".join(out)
def prepare_source(source, parser, dollar_math="auto", html_document=False):
"""Protect math before CommonMark and identify each actual image occurrence.
Code fences, indented code, inline code, HTML attributes and comments remain
literal. Markdown image references are expanded per occurrence, not per URL.
"""
from markdown_it.helpers import parseLinkDestination, parseLinkTitle
prefix = "RDR" + uuid.uuid4().hex.upper()
env = {}
tokens = [] if html_document else parser.parse(source, env)
offsets = [0]
for line in source.splitlines(keepends=True):
offsets.append(offsets[-1] + len(line))
literal = {
offsets[t.map[0]]: offsets[min(t.map[1], len(offsets) - 1)]
for t in tokens
if t.type in {"fence", "code_block"} and t.map
}
for definition in env.get("references", {}).values():
if definition.get("map"):
a, b = definition["map"]
literal[offsets[a]] = offsets[min(b, len(offsets) - 1)]
formulas, images, edits, notes = [], [], [], []
i = 0
while i < len(source):
if i in literal:
i = literal[i]
continue
if source[i] == "`" and not _escaped(source, i):
run = re.match(r"`+", source[i:])[0]
close = re.search(
r"(?<!`)" + re.escape(run) + r"(?!`)", source[i + len(run) :]
)
if close:
i += len(run) + close.end()
continue
if source.startswith("](", i):
# Markdown link destinations are not mathematical prose.
dest = parseLinkDestination(source, i + 2, len(source))
if dest.ok:
cursor = dest.pos
while cursor < len(source) and source[cursor].isspace():
cursor += 1
title = parseLinkTitle(source, cursor, len(source))
if title.ok:
cursor = title.pos
while cursor < len(source) and source[cursor].isspace():
cursor += 1
if cursor < len(source) and source[cursor] == ")":
i = cursor + 1
continue
if source[i] == "<":
tag = _TAG.match(source, i)
if tag:
raw = tag[0]
kind = re.match(r"<([A-Za-z][\w:-]*)", raw)
kind = kind[1].lower() if kind else ""
if kind in _DROP | {"code"}:
close = re.search(
r"</" + re.escape(kind) + r"\s*>", source[tag.end() :], re.I
)
if close:
i = tag.end() + close.end()
continue
if kind == "img":
src = _SRC.search(raw)
if src:
group = next(j for j in (1, 2, 3) if src[j] is not None)
start, end = i + src.start(group), i + src.end(group)
item = dict(
start=start,
end=end,
ref=html.unescape(src[group]),
kind="html",
token=prefix + "IMG" + str(len(images)),
)
images.append(item)
edits.append((start, end, item["token"]))
i = tag.end()
continue
if source.startswith("![", i) and not _escaped(source, i):
found = _markdown_image(source, i, parser, env)
if found:
end, ref, alt, title = found
item = dict(
start=i,
end=end,
ref=ref,
alt=alt,
title=title,
kind="markdown",
token=prefix + "IMG" + str(len(images)),
)
images.append(item)
edits.append((i, end, _image_markup(item, item["token"])))
i = end
continue
opening = next(
(s for s in ("\\(", "\\[", "$$", "$") if source.startswith(s, i)), None
)
if opening and not _escaped(source, i):
closing = {"\\(": "\\)", "\\[": "\\]", "$$": "$$", "$": "$"}[opening]
pos = i + len(opening)
while True:
end = source.find(closing, pos)
if end < 0:
break
if not _escaped(source, end):
break
pos = end + len(closing)
# Delimiters may not consume table cells or block boundaries.
# An explicit unclosed formula remains visible in its own cell.
boundary = re.search(
r"</?(?:table|thead|tbody|tfoot|tr|td|th|div|p|h[1-6]|li|ul|ol|pre|blockquote)\b[^>]*>|<br\b[^>]*>",
source[pos:],
re.I,
)
if boundary and end >= 0 and pos + boundary.start() < end:
notes.append({"code": "formula_block_boundary", "offset": i})
end = -1
if end >= 0:
body = source[i + len(opening) : end]
allowed = True
if opening == "$":
allowed = (
"\n" not in body
and not source.startswith("$$", end)
and _dollar_is_math(body, dollar_math)
)
if not allowed:
notes.append(
{"code": "literal_or_ambiguous_dollar", "offset": i}
)
elif "\n\n" in body and opening == "\\(":
allowed = False
if allowed:
token = prefix + "MATH" + str(len(formulas)) + "X"
formulas.append(
dict(
token=token,
tex=html.unescape(body),
raw=source[i : end + len(closing)],
display=opening in {"\\[", "$$"},
)
)
edits.append((i, end + len(closing), token))
i = end + len(closing)
continue
elif opening != "$":
# Preserve malformed explicit notation visibly, even through Markdown.
stop = source.find("\n\n", i)
stop = len(source) if stop < 0 else stop
tag_boundary = _TAG.search(source, i + len(opening))
if tag_boundary:
stop = min(stop, tag_boundary.start())
token = prefix + "MATH" + str(len(formulas)) + "X"
formulas.append(
dict(
token=token,
tex="",
raw=source[i:stop],
display=False,
malformed=True,
)
)
edits.append((i, stop, token))
i = stop
continue
# Consume an unrecognized delimiter as a unit (do not reinterpret $$ as $).
i += len(opening)
continue
i += 1
return _edit_source(source, edits), formulas, images, notes
def sanitize(soup):
from bs4 import Comment
removed = 0
for node in list(soup.find_all(string=lambda t: isinstance(t, Comment))):
node.extract()
for tag in list(soup.find_all(True)):
if tag.name is None:
continue
if tag.name in {"svg", "canvas", "math", "video", "audio"}:
raise RenderError(
"unsupported_embedded_media",
"Embedded media needs an explicit image asset; source retained.",
)
if tag.name in _DROP:
tag.decompose()
removed += 1
continue
if tag.name not in _ALLOWED:
tag.unwrap()
removed += 1
continue
attrs = {}
if tag.name in {"td", "th", "col", "colgroup"}:
for key in ("rowspan", "colspan", "span"):
if key in tag.attrs:
value = str(tag[key])
if not value.isdecimal() or not (
0 if key == "rowspan" else 1
) <= int(value) <= (65534 if key == "rowspan" else 1000):
raise RenderError(
"invalid_table_span",
"Invalid table cell span; source retained.",
)
attrs[key] = value
if tag.name == "img":
attrs.update({k: tag[k] for k in ("src", "alt", "title") if k in tag.attrs})
if tag.name == "ol" and str(tag.get("start", "")).lstrip("-").isdigit():
attrs["start"] = tag["start"]
# Retain safe alignment, but no CSS that can hide, move, clip or fetch.
declarations = dict(
(k.strip().lower(), v.strip().lower())
for k, v in re.findall(r"([\w-]+)\s*:\s*([^;]+)", str(tag.get("style", "")))
)
align = declarations.get("text-align", str(tag.get("align", "")).lower())
valign = declarations.get("vertical-align", str(tag.get("valign", "")).lower())
style = []
if align in {"left", "right", "center", "justify"}:
style.append("text-align:" + align)
if valign in {"top", "middle", "bottom", "baseline"}:
style.append("vertical-align:" + valign)
if style:
attrs["style"] = ";".join(style)
if dict(tag.attrs) != attrs:
removed += 1
tag.attrs = attrs
return removed
def _png_data(image):
from PIL import Image
image = image.convert("RGBA")
background = Image.new("RGBA", image.size, "white")
background.alpha_composite(image)
buf = io.BytesIO()
background.convert("RGB").save(buf, format="PNG")
return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode("ascii")
def resolve_alias(ref, aliases, *, tolerance=2, min_iou=0.98, events=None):
"""Exact first; a small coordinate jitter must match one unique occurrence.
This is a configurable rounding tolerance, not semantic figure localization.
An ambiguous or newly predicted region is never assigned to the nearest image.
"""
if ref in aliases:
return aliases[ref]
target = CROP.fullmatch(ref)
if not target:
raise RenderError("unmapped_crop", "Cannot map an image region to its source.")
page, *box = map(int, target.groups())
candidates = []
for key in aliases:
m = CROP.fullmatch(key)
if not m or int(m[1]) != page:
continue
other = list(map(int, m.groups()[1:]))
if max(abs(a - b) for a, b in zip(box, other)) > tolerance:
continue
intersection = max(0, min(box[2], other[2]) - max(box[0], other[0])) * max(
0, min(box[3], other[3]) - max(box[1], other[1])
)
area = max(0, box[2] - box[0]) * max(0, box[3] - box[1])
area2 = max(0, other[2] - other[0]) * max(0, other[3] - other[1])
union = area + area2 - intersection
if union and intersection / union >= min_iou:
candidates.append(key)
if len(candidates) != 1:
raise RenderError(
"ambiguous_crop" if candidates else "unmapped_crop",
"Cannot uniquely map an image region to its source.",
)
if events is not None:
events.append(
{
"code": "coordinate_rounding_match",
"predicted": ref,
"matched": candidates[0],
}
)
return aliases[candidates[0]]
class Renderer:
def __init__(
self,
browser,
mathjax,
*,
limits=None,
dollar_math="auto",
assets=None,
asset_root=None,
resize=None,
resize_name=None,
max_input_pixels=None,
):
from markdown_it import MarkdownIt
if dollar_math not in {"auto", "all", "off"}:
raise ValueError("dollar_math must be auto, all or off")
self.mathjax = Path(mathjax).resolve()
self.limits = limits or RenderLimits()
self.dollar_math = dollar_math
self.assets = dict(assets or {})
self.asset_root = Path(asset_root).resolve() if asset_root else None
self.resize = resize
self.resize_name = resize_name or (
"custom"
if resize
else "area_budget_estimate" if max_input_pixels else "unknown"
)
if max_input_pixels is not None and max_input_pixels <= 0:
raise ValueError("max_input_pixels must be positive")
self.max_input_pixels = max_input_pixels
self.page = browser.new_page(
viewport={"width": 1200, "height": 900}, device_scale_factor=1
)
self.page.set_default_timeout(self.limits.timeout_ms)
# No external assets, CSS or network dependencies in model-supplied HTML.
self.page.route("**/*", lambda route: route.abort())
self.md = MarkdownIt("commonmark", {"html": True}).enable(
["table", "strikethrough"]
)
def _asset(self, ref, owner, paths):
from PIL import Image
crop = _CROP_ASSET.fullmatch(ref)
if crop:
page, left, top, right, bottom = crop.groups()
page = int(page) if page else owner
left, top, right, bottom = map(int, (left, top, right, bottom))
if (
not 1 <= page <= len(paths)
or not 0 <= left < right <= 1000
or not 0 <= top < bottom <= 1000
):
raise RenderError(
"invalid_crop",
"An image crop has invalid coordinates; source retained.",
)
with Image.open(paths[page - 1]) as im:
box = (
round(left * im.width / 1000),
round(top * im.height / 1000),
round(right * im.width / 1000),
round(bottom * im.height / 1000),
)
if box[0] >= box[2] or box[1] >= box[3]:
raise RenderError(
"empty_crop", "An image crop is smaller than one pixel."
)
if (box[2] - box[0]) * (
box[3] - box[1]
) > self.limits.max_render_pixels:
raise RenderError(
"asset_limit",
"An image asset exceeds the rendering pixel budget.",
)
return _png_data(im.crop(box))
if ref.startswith("data:"):
header, sep, payload = ref.partition(",")
if not sep or not re.fullmatch(
r"data:image/(?:png|jpeg|webp|gif);base64", header, re.I
):
raise RenderError(
"unsupported_asset",
"An image format is unsupported; source retained.",
)
with Image.open(io.BytesIO(base64.b64decode(payload, validate=True))) as im:
if im.width * im.height > self.limits.max_render_pixels:
raise RenderError(
"asset_limit",
"An image asset exceeds the rendering pixel budget.",
)
return _png_data(im)
path = self.assets.get(ref)
if path is None and self.asset_root:
from urllib.parse import unquote, urlsplit
if not urlsplit(ref).scheme and not ref.startswith("//"):
candidate = (self.asset_root / unquote(ref)).resolve()
if candidate.is_relative_to(self.asset_root):
path = candidate
if path is None:
raise RenderError(
"unresolved_asset",
"An image needs a local asset mapping; source retained.",
)
with Image.open(path) as im:
if im.width * im.height > self.limits.max_render_pixels:
raise RenderError(
"asset_limit", "An image asset exceeds the rendering pixel budget."
)
return _png_data(im)
def render(self, md, owner, paths, directory, name):
directory = Path(directory)
directory.mkdir(parents=True, exist_ok=True)
if Path(name).name != name or not name:
raise ValueError("name must be a filename stem")
started = time.monotonic()
diag = {
"version": 2,
"status": "ERROR",
"math": 0,
"errors": 0,
"broken": 0,
"images": [],
"notes": [],
"limits": asdict(self.limits),
"resize_policy": self.resize_name,
}
(directory / (name + ".source.md")).write_text(md)
try:
result = self._render(md, owner, paths, directory, name, diag)
diag["status"] = "DEGRADED" if diag.get("formula_fallbacks") else "COMPLETE"
return result
except Exception as exc:
diag["error"] = {
"code": getattr(exc, "code", "render_failed"),
"type": type(exc).__name__,
"message": str(exc)[:1000],
}
if isinstance(exc, RenderError):
raise
raise RenderError(
"render_failed",
"Page rendering failed; source and diagnostics were retained.",
) from exc
finally:
diag["seconds"] = time.monotonic() - started
save(directory / (name + ".render.json"), diag)
def _render(self, md, owner, paths, directory, name, diag):
from bs4 import BeautifulSoup, Comment, NavigableString
from PIL import Image
if not 1 <= owner <= len(paths):
raise RenderError("invalid_owner", "Page index is outside the document.")
if len(md) > self.limits.max_source_chars:
raise RenderError(
"source_limit", "Page source exceeds the configured rendering limit."
)
probe = (
BeautifulSoup(md, "html.parser") if md.lstrip().startswith("<") else None
)
html_document = bool(
probe is not None
and probe.find(True)
and all(
not isinstance(x, NavigableString)
or isinstance(x, Comment)
or not str(x).strip()
for x in probe.contents
)
)
prepared, formulas, occurrences, notes = prepare_source(
md, self.md, self.dollar_math, html_document
)
diag["input_format"] = "html" if html_document else "markdown"
diag["notes"].extend(notes)
soup = BeautifulSoup(
prepared if html_document else self.md.render(prepared), "html.parser"
)
diag["sanitized_elements"] = sanitize(soup)
by_token = {x["token"]: x for x in occurrences}
seen = set()
for image in soup.find_all("img"):
key = image.get("src", "")
if key not in by_token or key in seen:
raise RenderError(
"image_mapping", "Cannot safely map image occurrences."
)
seen.add(key)
item = by_token[key]
image["src"] = self._asset(item["ref"], owner, paths)
image["data-image-id"] = key
if len(seen) != len(occurrences):
raise RenderError(
"image_mapping", "An image occurrence was lost during parsing."
)
by_math = {x["token"]: x for x in formulas}
if by_math:
pattern = re.compile(
"|".join(map(re.escape, sorted(by_math, key=len, reverse=True)))
)
found = []
for node in list(soup.find_all(string=pattern)):
if isinstance(node, Comment):
continue
pieces = pattern.split(str(node))
matches = list(pattern.finditer(str(node)))
for k, part in enumerate(pieces):
if part:
node.insert_before(NavigableString(part))
if k < len(matches):
item = by_math[matches[k][0]]
found.append(item["token"])
tag = soup.new_tag("span")
tag["data-math-id"] = item["token"]
tag["class"] = (
"math-display" if item["display"] else "math-inline"
)
tag.string = item["raw"]
node.insert_before(tag)
node.extract()
if sorted(found) != sorted(by_math):
raise RenderError(
"formula_mapping", "A formula was lost during parsing."
)
tables = soup.find_all("table")
expected_cells = len(soup.find_all(["td", "th"]))
columns = max(
(
sum(
int(c.get("colspan", 1))
for c in row.find_all(["td", "th"], recursive=False)
)
for row in soup.find_all("tr")
),
default=1,
)
with Image.open(paths[owner - 1]) as original:
width = max(original.width, columns * 120 + 56)
document = (
'<!doctype html><html><head><meta charset="utf-8"><style>'
+ _CSS
+ '</style></head><body><div id="capture" style="width:'
+ str(width)
+ 'px">'
+ str(soup)
+ "</div></body></html>"
)
# Persist complete render input before any resource/readability rejection.
(directory / (name + ".html")).write_text(document)
if width > self.limits.max_side:
raise RenderError(
"size_limit",
"Rendered page exceeds the configured size limit; complete source retained.",
)
page = self.page
page.set_viewport_size({"width": width, "height": 900})
page.set_content(document, wait_until="domcontentloaded")
diag["expected_formulas"] = len(formulas)
formula_results = []
if formulas:
if not self.mathjax.is_file():
raise RenderError("mathjax_missing", "Local MathJax bundle is missing.")
page.evaluate(
"window.MathJax={startup:{typeset:false},svg:{fontCache:'none'},tex:{maxBuffer:100000,packages:['base','ams','newcommand']}}"
)
page.add_script_tag(path=str(self.mathjax))
page.wait_for_function(
"window.MathJax && MathJax.startup && MathJax.startup.promise"
)
formula_results = page.evaluate(
"""async ({items,timeout})=>{
const work=async()=>{
await MathJax.startup.promise;
if(typeof MathJax.tex2svgPromise!=='function') throw Error('MathJax SVG API unavailable');
const results=[];
for(const item of items){
const target=document.querySelector('[data-math-id="'+item.token+'"]');
let error=null;
try{
if(item.malformed) throw Error('Unclosed formula delimiter');
if(!item.tex.trim()) throw Error('Empty formula');
const rendered=await MathJax.tex2svgPromise(item.tex,{display:item.display});
if(rendered.querySelector('[data-mml-node="merror"],mjx-merror,[data-mjx-error]')) throw Error('Invalid TeX');
// This screenshot renderer needs only SVG. Standalone tex2svg
// may emit assistive MathML without the CSS that hides it.
rendered.querySelectorAll('mjx-assistive-mml').forEach(node=>node.remove());
target.replaceChildren(rendered);
}catch(e){error=String(e.message||e);target.textContent=item.raw;target.classList.add('math-fallback');}
results.push({id:item.token,rendered:!error,error});
}
return results;
};
let timer;try{return await Promise.race([work(),new Promise((_,reject)=>{timer=setTimeout(()=>reject(Error('Formula rendering timeout')),timeout)})]);}finally{clearTimeout(timer);}
}""",
{"items": formulas, "timeout": self.limits.timeout_ms},
)
diag["formulas"] = formula_results
diag["formula_fallbacks"] = sum(not x["rendered"] for x in formula_results)
page.evaluate(
"""async timeout=>{
let timer;try{await Promise.race([(async()=>{await document.fonts.ready;await Promise.all([...document.images].map(i=>i.decode()));await new Promise(r=>requestAnimationFrame(()=>requestAnimationFrame(r)));})(),new Promise((_,reject)=>{timer=setTimeout(()=>reject(Error('Image/font readiness timeout')),timeout)})]);}finally{clearTimeout(timer);}
}""",
self.limits.timeout_ms,
)
# Allow intrinsically wide math/tables to grow once, within the resource budget.
needed = page.evaluate("document.documentElement.scrollWidth")
if needed > width:
width = needed + 28
if width > self.limits.max_side:
raise RenderError(
"size_limit",
"Rendered content is too wide; complete source retained.",
)
page.set_viewport_size({"width": width, "height": 900})
page.locator("#capture").evaluate('(e,w)=>e.style.width=w+"px"', width)
layout = page.evaluate("""()=>{
const root=document.getElementById('capture');const area=root.getBoundingClientRect();
const boxes=[...root.querySelectorAll('table,td,th,img,[data-math-id]')];
const bad=boxes.filter(e=>{const b=e.getBoundingClientRect(),s=getComputedStyle(e);return !b.width||!b.height||s.display==='none'||s.visibility==='hidden'||Number(s.opacity)===0||b.left<0||b.right>area.right+.5;}).map(e=>({tag:e.tagName,id:e.dataset.imageId||e.dataset.mathId||null}));
const fonts=[];const walker=document.createTreeWalker(root,NodeFilter.SHOW_TEXT);let n;
while(n=walker.nextNode()){if(n.textContent.trim()&&!n.parentElement.closest('mjx-container')){const s=getComputedStyle(n.parentElement);fonts.push(parseFloat(s.fontSize));}}
const cellHeights=[...root.querySelectorAll('td,th')].map(e=>e.getBoundingClientRect().height);
return {math:root.querySelectorAll('mjx-container').length,broken:[...root.querySelectorAll('img')].filter(i=>!i.complete||!i.naturalWidth).length,
tables:root.querySelectorAll('table').length,cells:cellHeights.length,invisible_or_clipped:bad,
min_font_px:fonts.length?fonts.reduce((a,b)=>Math.min(a,b),Infinity):18,min_cell_height_px:cellHeights.length?cellHeights.reduce((a,b)=>Math.min(a,b),Infinity):null,
width:document.documentElement.scrollWidth,height:document.documentElement.scrollHeight,
images:[...root.querySelectorAll('img')].map(i=>{const b=i.getBoundingClientRect();return {id:i.dataset.imageId,x:b.x,y:b.y,w:b.width,h:b.height}})};
}""")
diag.update(layout)
diag["errors"] = diag["formula_fallbacks"]
if layout["tables"] != len(tables) or layout["cells"] != expected_cells:
raise RenderError(
"table_structure",
"Browser parsing changed the table structure; source retained.",
)
if layout["invisible_or_clipped"] or layout["broken"]:
raise RenderError(
"invisible_content",
"Some page content is not visible; source retained.",
)
if layout["math"] + diag["formula_fallbacks"] != len(formulas):
raise RenderError(
"formula_coverage", "Formula rendering coverage is incomplete."
)
w, h = layout["width"], layout["height"]
# Snapshot has no live external scripts or assets; it opens offline as rendered.
(directory / (name + ".html")).write_text(
page.evaluate(
"() => { const root=document.documentElement.cloneNode(true); root.querySelectorAll('script').forEach(e=>e.remove()); return '<!doctype html>'+root.outerHTML; }"
)
)
if max(w, h) > self.limits.max_side or w * h > self.limits.max_render_pixels:
raise RenderError(
"size_limit",
"Rendered page exceeds the configured size limit; complete HTML retained.",
)
target = (w, h)
if not self.resize and not self.max_input_pixels:
diag["notes"].append({"code": "model_resize_unspecified"})
if self.resize:
try:
target = tuple(self.resize(w, h))
except Exception as exc:
raise RenderError(
"model_resize_limit",
"Page exceeds the model image-size limits; complete HTML retained.",
) from exc
elif self.max_input_pixels:
scale = min(1, math.sqrt(self.max_input_pixels / (w * h)))
target = (max(1, math.floor(w * scale)), max(1, math.floor(h * scale)))
if len(target) != 2 or min(target) <= 0:
raise RenderError(
"resize_policy", "Invalid target dimensions from resize policy."
)
scale = min(target[0] / w, target[1] / h)
font = layout["min_font_px"] * scale
cell = (
layout["min_cell_height_px"] * scale
if layout["min_cell_height_px"]
else None
)
diag["readability"] = {
"input_size": [w, h],
"model_size": list(target),
"scale": scale,
"effective_font_px": font,
"effective_cell_height_px": cell,
"model_resize_known": bool(self.resize),
"estimated_from_pixel_budget": bool(
not self.resize and self.max_input_pixels
),
"thresholds_are_heuristic": True,
}
if font < self.limits.min_effective_font_px or (
cell is not None and cell < self.limits.min_effective_cell_height_px
):
raise RenderError(
"readability_limit",
"Page text would be too small after resizing; complete HTML retained.",
)
aliases, edits = {}, []
for image in layout["images"]:
item = by_token[image["id"]]
image["ref"] = item["ref"]
box = [
round(image["x"] / w * 1000),
round(image["y"] / h * 1000),
round((image["x"] + image["w"]) / w * 1000),
round((image["y"] + image["h"]) / h * 1000),
]
if not 0 <= box[0] < box[2] <= 1000 or not 0 <= box[1] < box[3] <= 1000:
raise RenderError(
"crop_quantization",
"An image is too small for normalized coordinate mapping.",
)
alias = f"page_{owner}/images/bbox_" + "_".join(map(str, box)) + ".jpg"
if alias in aliases:
raise RenderError(
"crop_alias_collision",
"Image positions cannot be uniquely represented.",
)
aliases[alias] = item["ref"]
edits.append((item["start"], item["end"], _image_markup(item, alias)))
rewritten = _edit_source(md, edits)
path = directory / (name + ".png")
page.screenshot(path=str(path), full_page=True, timeout=self.limits.timeout_ms)
with Image.open(path) as rendered:
if rendered.size != (w, h):
raise RenderError(
"screenshot_dimensions",
"Screenshot dimensions differ from image coordinate mapping.",
)
(directory / (name + ".rewritten.md")).write_text(rewritten)
save(directory / (name + ".aliases.json"), aliases)
return RenderResult(str(path), rewritten, aliases, diag)
# Command-line interface.
def main():
parser = argparse.ArgumentParser(
description="OvisOCR3 document-level cross-page reconstruction"
)
parser.add_argument("images", nargs="+", help="Page images in reading order")
parser.add_argument("--model", default=".")
parser.add_argument("--output", required=True)
parser.add_argument(
"--stage1-prompt",
default="document_structured.txt",
choices=[
"document_markdown",
"document_markdown.txt",
"document_structured",
"document_structured.txt",
],
)
parser.add_argument(
"--mathjax",
default=None,
help="Local MathJax bundle; defaults to assets/mathjax.js beside this script",
)
args = parser.parse_args()
result = reconstruct_document(
args.images,
args.output,
model_path=args.model,
stage1_prompt=args.stage1_prompt,
mathjax=args.mathjax,
)
print(json.dumps(result, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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