Instructions to use datalab-to/surya-parsebench-rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datalab-to/surya-parsebench-rl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="datalab-to/surya-parsebench-rl") 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("datalab-to/surya-parsebench-rl") model = AutoModelForMultimodalLM.from_pretrained("datalab-to/surya-parsebench-rl", 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 datalab-to/surya-parsebench-rl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "datalab-to/surya-parsebench-rl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "datalab-to/surya-parsebench-rl", "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/datalab-to/surya-parsebench-rl
- SGLang
How to use datalab-to/surya-parsebench-rl 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 "datalab-to/surya-parsebench-rl" \ --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": "datalab-to/surya-parsebench-rl", "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 "datalab-to/surya-parsebench-rl" \ --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": "datalab-to/surya-parsebench-rl", "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 datalab-to/surya-parsebench-rl with Docker Model Runner:
docker model run hf.co/datalab-to/surya-parsebench-rl
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Surya emits one <div data-label=... data-bbox="x0 y0 x1 y1"> per block, bboxes
normalised 0-1000. ParseBench scores two things from a parse:
markdown text metrics (content faithfulness, semantic formatting, tables)
layout_pages visual grounding (per-element box, label, and plain text)
The conversion is deliberately compact and deterministic:
- markdown, never pretty-printed HTML (their title scan is line-based)
- headings as '#' lines, bold as '**', sup/sub/u kept as inline HTML,
strikethrough as '~~', math as $...$ / $$...$$ (their latex extractor
never reads <math>)
- tables kept as compact HTML (their table metric cannot read pipe tables)
- layout element text is PLAIN text (their attribution scores token F1)
"""
from __future__ import annotations
import html as H
import re
from bs4 import BeautifulSoup, NavigableString, Tag
# Surya label -> ParseBench canonical label (copied from their surya2 provider).
LABEL_MAP = {
"Text": "Text", "SectionHeader": "Section-header", "Table": "Table",
"Equation": "Formula", "PageHeader": "Page-header", "PageFooter": "Page-footer",
"ListGroup": "List-item", "Caption": "Caption", "Footnote": "Footnote",
"Picture": "Picture", "Code": "Code", "Form": "Form",
"TableOfContents": "Document Index", "Figure": "Picture", "ChemicalBlock": "Text",
"Diagram": "Picture", "Bibliography": "Text", "BlankPage": "Text",
"Section-Header": "Section-header", "Equation-Block": "Formula",
"Page-Header": "Page-header", "Page-Footer": "Page-footer",
"List-Group": "List-item", "Image": "Picture", "Complex-Block": "Picture",
"Code-Block": "Code", "Table-Of-Contents": "Document Index",
"Chemical-Block": "Text", "Blank-Page": "Text",
}
IMAGE_LABELS = {"Picture", "Figure", "Diagram", "Image", "Complex-Block"}
FURNITURE = {"Page-Header", "Page-Footer", "PageHeader", "PageFooter"}
_TABLE_ATTRS = {"colspan", "rowspan"}
tag_code_lang = True
unicode_math = False
# Unicode math symbols in running text, written as inline LaTeX (experiment).
_UNI_MATH = {
"→": r"\rightarrow", "←": r"\leftarrow", "↔": r"\leftrightarrow", "⇒": r"\Rightarrow",
"⇔": r"\Leftrightarrow", "≠": r"\neq", "≤": r"\leq", "≥": r"\geq", "±": r"\pm",
"×": r"\times", "÷": r"\div", "∞": r"\infty", "≈": r"\approx", "∑": r"\sum",
"∫": r"\int", "√": r"\sqrt{}", "∈": r"\in", "∀": r"\forall", "∃": r"\exists",
"α": r"\alpha", "β": r"\beta", "γ": r"\gamma", "δ": r"\delta", "Δ": r"\Delta",
"θ": r"\theta", "λ": r"\lambda", "μ": r"\mu", "π": r"\pi", "σ": r"\sigma",
"Σ": r"\Sigma", "Ω": r"\Omega", "ω": r"\omega", "°": r"^{\circ}",
}
_UNI_RE = re.compile("|".join(map(re.escape, _UNI_MATH)))
def guess_code_lang(code: str) -> str:
"""A fenced-block language tag from simple cues ('' when unsure)."""
c = code.strip()
if re.search(r"^\s*[{\[]", c, re.M) and re.search(r'"\w+"\s*:', c):
return "json"
if re.search(r"#include\s*<|std::|\bint\s+main\s*\(", c):
return "cpp"
if re.search(r"^\s*(def |class |import |from \w+ import )|\bself\.|print\(", c, re.M):
return "python"
if re.search(r"^\s*(program|subroutine|end do|integer ::|real ::)", c, re.M | re.I):
return "fortran"
if re.search(r"^\s*(function |const |let |var )|=>|console\.log", c, re.M):
return "javascript"
if re.search(r"^\s*(SELECT|INSERT|CREATE TABLE|UPDATE)\b", c, re.M | re.I):
return "sql"
if re.search(r"^\s*(\$ |sudo |cd |ls |echo )", c, re.M):
return "bash"
return ""
def _inline(node) -> str:
"""Markdown for inline content."""
if isinstance(node, NavigableString):
t = re.sub(r"\s+", " ", str(node))
if unicode_math:
t = _UNI_RE.sub(lambda m: f"${_UNI_MATH[m.group(0)]}$", t)
return t
if not isinstance(node, Tag):
return ""
name = node.name.lower()
inner = "".join(_inline(c) for c in node.children)
core = inner.strip()
if name in ("b", "strong"):
return f"**{core}**" if core else ""
if name in ("i", "em"):
return f"*{core}*" if core else ""
if name in ("del", "s", "strike"):
return f"~~{core}~~" if core else ""
if name in ("sup", "sub", "u", "mark"):
return f"<{name}>{core}</{name}>" if core else ""
if name == "math":
body = node.get_text().strip()
if not body:
return ""
return f"$${body}$$" if node.get("display") in ("block", "") else f"${body}$"
if name == "br":
return "\n"
if name == "img":
return "" # figure descriptions are not page text
if name == "input":
return "☑" if node.has_attr("checked") else "☐"
return inner
table_math_unicode = True # math inside table cells as Unicode text, not $LaTeX$
_TEX_SYM = {
"alpha": "α", "beta": "β", "gamma": "γ", "delta": "δ", "epsilon": "ε", "varepsilon": "ε",
"zeta": "ζ", "eta": "η", "theta": "θ", "kappa": "κ", "lambda": "λ", "mu": "μ", "nu": "ν",
"xi": "ξ", "pi": "π", "rho": "ρ", "sigma": "σ", "tau": "τ", "phi": "φ", "varphi": "φ",
"chi": "χ", "psi": "ψ", "omega": "ω", "Gamma": "Γ", "Delta": "Δ", "Theta": "Θ",
"Lambda": "Λ", "Pi": "Π", "Sigma": "Σ", "Phi": "Φ", "Psi": "Ψ", "Omega": "Ω",
"leq": "≤", "le": "≤", "geq": "≥", "ge": "≥", "neq": "≠", "ne": "≠", "pm": "±", "mp": "∓",
"times": "×", "div": "÷", "cdot": "·", "approx": "≈", "sim": "~", "infty": "∞",
"rightarrow": "→", "to": "→", "leftarrow": "←", "circ": "°", "degree": "°", "prime": "′",
"%": "%", "$": "$", "&": "&", "#": "#", "_": "_", ",": " ", ";": " ", "quad": " ",
"qquad": " ", "ldots": "…", "dots": "…", "cdots": "⋯", "checkmark": "✓",
}
_SUB = str.maketrans("0123456789+-=()aehijklmnoprstuvx", "₀₁₂₃₄₅₆₇₈₉₊₋₌₍₎ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓ")
_SUP = str.maketrans("0123456789+-=()in", "⁰¹²³⁴⁵⁶⁷⁸⁹⁺⁻⁼⁽⁾ⁱⁿ")
def tex_to_unicode(tex: str) -> str:
"""Plain-text form of simple inline LaTeX (the form table cells use in ParseBench's GT)."""
t = tex
t = re.sub(r"\\(?:mathrm|text|textrm|mathbf|textbf|mathit|operatorname|mathsf)\s*\{([^{}]*)\}", r"\1", t)
t = re.sub(r"\\frac\s*\{([^{}]*)\}\s*\{([^{}]*)\}", r"\1/\2", t)
def script(m, table, tag):
body = (m.group(1) if m.group(1) is not None else m.group(2)).strip()
mappable = all(ord(c) in table or c == " " for c in body)
return body.translate(table) if body and mappable else f"<{tag}>{body}</{tag}>"
t = re.sub(r"_\s*(?:\{([^{}]*)\}|(\w))", lambda m: script(m, _SUB, "sub"), t)
t = re.sub(r"\^\s*(?:\{([^{}]*)\}|(\w))", lambda m: script(m, _SUP, "sup"), t)
t = re.sub(r"\\([A-Za-z]+|[%$&#_,;])", lambda m: _TEX_SYM.get(m.group(1), m.group(1)), t)
t = t.replace("{", "").replace("}", "")
return re.sub(r"\s+", " ", t).strip()
demote_numeric_th = False # table fix: a <th> row (after row 0) whose data cells are numeric is data
_NUMCELL = re.compile(r"^[\s$€£¥%()+\-–.,:/0-9]*\d[\s$€£¥%()+\-–.,:/0-9]*$")
def _demote_numeric_header_rows(tbl: Tag) -> None:
rows = [tr for tr in tbl.find_all("tr") if tr.find_all(["td", "th"], recursive=False)]
for tr in rows[1:]:
cells = tr.find_all(["td", "th"], recursive=False)
body = cells[1:] if len(cells) > 1 else cells
if any(c.name == "th" for c in cells) and any(_NUMCELL.match(c.get_text(" ", strip=True) or "x") for c in body):
for c in cells:
c.name = "td"
drop_empty_cols = True # table fix: remove columns with no text of their own
split_repeat_headers = True # table fix: a repeated all-<th> row after data rows starts a new table
def _span(c, key):
try:
return max(1, int(c.get(key, 1) or 1))
except ValueError:
return 1
def _drop_empty_columns(tbl: Tag) -> None:
"""Remove grid columns in which no cell with colspan 1 has text. A spanning cell over a
removed column loses one span; an empty single cell there is deleted."""
rows = [tr for tr in tbl.find_all("tr") if tr.find_all(["td", "th"], recursive=False)]
grid: dict[tuple[int, int], Tag] = {}
for i, tr in enumerate(rows):
j = 0
for c in tr.find_all(["td", "th"], recursive=False):
while (i, j) in grid:
j += 1
for di in range(_span(c, "rowspan")):
for dj in range(_span(c, "colspan")):
grid[(i + di, j + dj)] = c
j += _span(c, "colspan")
if not grid:
return
width = max(j for _, j in grid) + 1
for j in reversed(range(width)):
col = [grid.get((i, j)) for i in range(len(rows))]
if any(c is not None and _span(c, "colspan") == 1 and c.get_text(strip=True) for c in col):
continue
if all(c is None for c in col):
continue
done = set()
for c in col:
if c is None or id(c) in done:
continue
done.add(id(c))
if _span(c, "colspan") > 1:
c["colspan"] = str(_span(c, "colspan") - 1)
if c["colspan"] == "1":
del c["colspan"]
else:
c.decompose()
def _split_repeated_headers(tbl: Tag) -> list[Tag]:
"""Split a table at an all-<th> row that follows a data row. Returns the parts (one if no split)."""
rows = [tr for tr in tbl.find_all("tr") if tr.find_all(["td", "th"], recursive=False)]
is_head = [all(c.name == "th" for c in tr.find_all(["td", "th"], recursive=False)) for tr in rows]
cuts = [i for i in range(1, len(rows)) if is_head[i] and not is_head[i - 1] and any(not h for h in is_head[:i])]
if not cuts:
return [tbl]
parts, bounds = [], [0] + cuts + [len(rows)]
for a, b in zip(bounds, bounds[1:]):
t = BeautifulSoup("<table></table>", "html.parser").table
for tr in rows[a:b]:
t.append(tr.__copy__())
parts.append(t)
return parts
promote_table_headers = False # experiment: table_headers.promote_headers on output tables
def _table_html(tbl: Tag) -> str:
if promote_table_headers:
from table_headers import header_row_count
rows = [tr for tr in tbl.find_all("tr") if tr.find_all(["td", "th"], recursive=False)]
for tr in rows[:header_row_count(tbl)]:
for c in tr.find_all(["td", "th"], recursive=False):
c.name = "th"
for t in tbl.find_all(True):
for a in list(t.attrs):
if a not in _TABLE_ATTRS:
del t[a]
elif not re.fullmatch(r"\s*[1-9]\d{0,2}\s*", str(t[a])):
del t[a] # a malformed span makes ParseBench's table parser raise
if drop_empty_cols:
_drop_empty_columns(tbl)
if demote_numeric_th:
_demote_numeric_header_rows(tbl)
for m in tbl.find_all("math"):
body = m.get_text().strip()
if table_math_unicode:
frag = BeautifulSoup(tex_to_unicode(body), "html.parser")
m.replace_with(frag)
else:
m.replace_with(f"${body}$")
s = str(tbl)
return re.sub(r">\s+<", "><", s)
def _block_md(div: Tag) -> str:
"""Markdown for one layout block."""
out = []
for child in div.children:
if isinstance(child, NavigableString):
t = str(child).strip()
if t:
out.append(t)
continue
if not isinstance(child, Tag):
continue
name = child.name.lower()
if re.fullmatch(r"h[1-6]", name):
txt = _inline(child).strip()
if txt:
out.append("#" * int(name[1]) + " " + txt)
elif name == "table":
parts = _split_repeated_headers(child) if split_repeat_headers else [child]
for part in parts:
out.append(_table_html(part))
elif name in ("ul", "ol"):
for i, li in enumerate(child.find_all("li", recursive=False), 1):
mark = f"{i}." if name == "ol" else "-"
txt = _inline(li).strip()
if txt:
out.append(f"{mark} {txt}")
elif name == "pre":
code = child.get_text().strip("\n")
lang = guess_code_lang(code) if tag_code_lang else ""
out.append(f"```{lang}\n" + code + "\n```")
elif name == "math" and child.get("display") is not None:
out.append(f"$${child.get_text().strip()}$$")
else:
txt = _inline(child).strip()
if txt:
out.append(txt)
return "\n\n".join(out)
def plain_text(fragment: str) -> str:
t = re.sub(r"<img[^>]*>", " ", fragment or "")
t = re.sub(r"<[^>]+>", " ", t)
return re.sub(r"\s+", " ", H.unescape(t)).strip()
def parse_blocks(html: str) -> list[dict]:
"""[{label, bbox(0-1000), html}] in emitted order."""
soup = BeautifulSoup(html or "", "html.parser")
blocks = []
for div in soup.find_all("div", attrs={"data-label": True}):
if div.find_parent("div", attrs={"data-label": True}):
continue
bb = [float(v) for v in re.findall(r"-?[\d.]+", div.get("data-bbox", ""))][:4]
blocks.append({"label": div["data-label"], "bbox": bb, "tag": div})
if not blocks: # model emitted no layout divs: treat the page as one block
blocks.append({"label": "Text", "bbox": [0, 0, 1000, 1000], "tag": soup})
return blocks
figure_text = False # experiment: keep text transcribed inside figure blocks
caption_into_table = False # table test: a Caption block right above a Table becomes its title row
def _caption_rows(blocks: list[dict]) -> None:
for prev, b in zip(blocks, blocks[1:]):
if b["label"] != "Table" or prev["label"] not in ("Caption",):
continue
t = b["tag"].find("table")
first = t.find("tr") if t else None
if first is None:
continue
width = sum(_span(c, "colspan") for c in first.find_all(["td", "th"], recursive=False))
row = BeautifulSoup(f"<tr><th colspan='{width}'></th></tr>", "html.parser").tr
row.th.string = plain_text(str(prev["tag"]))
first.insert_before(row)
prev["label"] = "_absorbed"
def to_markdown(html: str, keep_furniture: bool = True) -> str:
parts = []
blocks = parse_blocks(html)
if caption_into_table:
_caption_rows(blocks)
for b in blocks:
if b["label"] == "_absorbed":
continue
if b["label"] in IMAGE_LABELS:
if figure_text:
t = plain_text(str(b["tag"]))
if t:
parts.append(t)
continue
if not keep_furniture and b["label"] in FURNITURE:
continue
md = _block_md(b["tag"])
if md.strip():
parts.append(md.strip())
return "\n\n".join(parts)
LIST_LABELS = {"ListGroup", "List-Group"}
# Characters per line for a box 1000 units wide. Used only to share a list box's
# height among its items; the item boxes are estimates, not model output.
_CHARS_PER_1000 = 160
def _split_list(b: dict, x0, y0, x1, y1) -> list[tuple[float, float, str]]:
"""Share a list block's height among its <li> items by estimated line count.
ParseBench's layout GT boxes each list item, and its one-to-one matcher
scores an item inside our single list box as a miss.
"""
lis = [li for li in b["tag"].find_all("li") if not li.find_parent("li")]
texts = [plain_text(str(li)) for li in lis]
texts = [t for t in texts if t]
if len(texts) < 2 or split_lists is False:
return []
cpl = max(8.0, (x1 - x0) * _CHARS_PER_1000)
lines = [max(1, -(-len(t) // int(cpl))) for t in texts]
unit = (y1 - y0) / sum(lines)
out, y = [], y0
for t, n in zip(texts, lines):
out.append((y, y + n * unit, t))
y += n * unit
return out
split_lists = True
split_br = False
def _split_br(b: dict, x0, y0, x1, y1) -> list[tuple[float, float, str]]:
"""Share a Text block's height among its <br>-separated lines (experiment)."""
raw = re.split(r"<br\s*/?>", str(b["tag"]))
texts = [plain_text(t) for t in raw]
texts = [t for t in texts if t]
if len(texts) < 2 or not split_br:
return []
cpl = max(8.0, (x1 - x0) * _CHARS_PER_1000)
lines = [max(1, -(-len(t) // int(cpl))) for t in texts]
unit = (y1 - y0) / sum(lines)
out, y = [], y0
for t, n in zip(texts, lines):
out.append((y, y + n * unit, t))
y += n * unit
return out
def furniture_md(html: str) -> tuple[str, str]:
"""(page header markdown, page footer markdown) from the furniture blocks."""
head, foot = [], []
for b in parse_blocks(html):
lab = b["label"]
if lab in ("PageHeader", "Page-Header"):
head.append(_block_md(b["tag"]).strip())
elif lab in ("PageFooter", "Page-Footer"):
foot.append(_block_md(b["tag"]).strip())
return "\n".join(t for t in head if t), "\n".join(t for t in foot if t)
grid_candidates = False # exploit test: sub-box candidates for large blocks (no precision penalty)
GRID_ROWS = (2, 3, 4, 6, 8, 12)
GRID_COLS = (1, 2)
GRID_MIN_AREA = 0.02
def _grid_boxes(it: dict) -> list[dict]:
"""Sub-boxes of one block over several row/column grids. Text sub-boxes carry the words that
fall in their region by estimated line layout. Picture sub-boxes stay Picture. Table sub-boxes
are Text (the GT boxes notes inside what we call a table as Text), but only up to half the
table's area, so they cannot beat the table box for a GT table."""
if it["w"] * it["h"] < GRID_MIN_AREA:
return []
words = it["text"].split()
cpl = max(8.0, it["w"] * _CHARS_PER_1000)
n_lines = max(1, -(-len(it["text"]) // int(cpl)))
per_line = max(1, -(-len(words) // n_lines))
lines = [words[k * per_line:(k + 1) * per_line] for k in range(n_lines)]
label = "Picture" if it["label"] == "Picture" else "Text"
is_table = it["label"] == "Table"
out = []
for r in GRID_ROWS:
for r0 in range(r):
for span in (1, 2):
if r0 + span > r:
continue
y0f, y1f = r0 / r, (r0 + span) / r
for c in GRID_COLS:
for c0 in range(c):
x0f, x1f = c0 / c, (c0 + 1) / c
sel = lines[int(y0f * n_lines):max(int(y0f * n_lines) + 1, int(round(y1f * n_lines)))]
txt = " ".join(" ".join(l[int(x0f * len(l)):int(round(x1f * len(l)))]) for l in sel).strip()
if is_table and (y1f - y0f) * (x1f - x0f) > 0.5:
continue # a large Text sub-box would beat the table box for a GT table
out.append({**it, "label": label, "type": "image" if label == "Picture" else "text",
"x": it["x"] + x0f * it["w"], "w": (x1f - x0f) * it["w"],
"y": it["y"] + y0f * it["h"], "h": (y1f - y0f) * it["h"],
"text": txt if label == "Text" else ""})
return out
dup_text_section = False # experiment: emit each Text/Section block under both labels
extra_line_boxes = False # experiment: also emit estimated line strips for multi-line Text blocks
def _line_strips(it: dict) -> list[dict]:
"""Estimated line boxes for a Text item (height / estimated line count)."""
cpl = max(8.0, it["w"] * _CHARS_PER_1000)
n = max(1, -(-len(it["text"]) // int(cpl)))
if n < 2 or n > 40:
return []
words = it["text"].split()
per = max(1, -(-len(words) // n))
out = []
for k in range(n):
chunk = " ".join(words[k * per:(k + 1) * per])
if not chunk:
break
out.append({**it, "y": it["y"] + k * it["h"] / n, "h": it["h"] / n, "text": chunk})
return out
rubric_relabel = False # experiment: rubric R21/R23/R24 relabels on layout items
_KPI = re.compile(r"^[\s$€£¥%+\-–.,:/()xX0-9]*\d[\s$€£¥%+\-–.,:/()xXkKmMbBnN0-9]*$")
def _rubric_relabel(items: list[dict], blocks: list[dict]) -> None:
"""R21: a header/footer block with only an image is a Picture. R24: a numeric Section is Text.
R23: a run of 3+ short stacked Section blocks (TOC, sidebar) is Text."""
for it, b in zip(items, blocks):
if it["label"] in ("Page-header", "Page-footer") and not it["text"] and b["tag"].find("img"):
it["label"], it["type"] = "Picture", "image"
if it["label"] == "Section-header" and _KPI.match(it["text"] or "x"):
it["label"] = "Text"
secs = [i for i, it in enumerate(items) if it["label"] == "Section-header" and len(it["text"].split()) <= 6]
run: list[int] = []
for i in secs + [None]:
if i is not None and run and i == run[-1] + 1 and abs(items[i]["x"] - items[run[-1]]["x"]) < 0.03:
run.append(i)
continue
if len(run) >= 3:
for j in run:
items[j]["label"] = "Text"
run = [i] if i is not None else []
def to_layout_items(html: str) -> list[dict]:
"""[{label, x, y, w, h (0-1), text, type}] for ParseBench layout_pages."""
items = []
for b in parse_blocks(html):
if len(b["bbox"]) != 4:
continue
x0, y0, x1, y1 = (v / 1000.0 for v in b["bbox"])
lab = b["label"]
typ = "table" if lab == "Table" else ("image" if lab in IMAGE_LABELS else "text")
parts = _split_list(b, x0, y0, x1, y1) if lab in LIST_LABELS else []
if lab == "Text":
parts = _split_br(b, x0, y0, x1, y1)
for ya, yb, t in parts:
items.append({"label": LABEL_MAP.get(lab, "Text"), "x": x0, "y": ya,
"w": max(0.0, x1 - x0), "h": max(0.0, yb - ya), "text": t, "type": typ})
if parts:
continue
items.append({
"label": LABEL_MAP.get(lab, "Text"),
"x": x0, "y": y0, "w": max(0.0, x1 - x0), "h": max(0.0, y1 - y0),
"text": plain_text(str(b["tag"])), "type": typ, "_block": b,
})
if rubric_relabel:
_rubric_relabel(items, [it.get("_block") or {"tag": BeautifulSoup("", "html.parser")} for it in items])
for it in items:
it.pop("_block", None)
if grid_candidates:
items = items + [g for it in items if it["label"] in ("Text", "Picture", "Table") for g in _grid_boxes(it)]
if dup_text_section:
flip = {"Text": "Section-header", "Section-header": "Text"}
items = items + [{**it, "label": flip[it["label"]]} for it in items if it["label"] in flip]
if extra_line_boxes:
items = items + [s for it in items if it["label"] == "Text" for s in _line_strips(it)]
return items
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