Hugging Face's logo

Datasets:
uv-scripts
/
ocr

ArXiv:
Tags:
uv-script
ocr
extraction
vision-language-model
document-processing
hf-jobs
Dataset card Files
xet
 Community
12
ocr / CLAUDE.md
davanstrien's picture
davanstrien HF Staff
Sync from GitHub via hub-sync
8f4316a verified
preview code
|
Raw
History Blame Contribute Delete
23.2 kB

OCR Scripts — Development Notes

Dev notes for the ocr/ recipes: the conventions to follow, the per-script gotchas (the "why" behind each script's quirks), and the internal tooling. Runnable examples live in each script's docstring and README.md; benchmark result tables live in OCR-BENCHMARK.md.

Conventions & invariants

Read this before adding or changing a recipe. Each rule maps to a failure we've actually hit; the planned self-review skill (see Deferred) just enforces this list.

  • Catalog entry. Adding or changing a recipe means updating models.json (script → model, params, backend, image pins, language claim) and, if the language claim changed, LANGUAGES.md. Language fields record what the model card claims (with an evidence level), never inferred coverage. Hand-maintained for now; if drift becomes a problem, the follow-up is generating the README table from the JSON.
  • Self-contained single file. Each recipe is one PEP 723 UV script runnable from a raw URL (hf jobs uv run <url>). No shared importable local module (the job env only gets the one file). Extra pip deps are fine — pin them. A heavy/stable/shared subsystem may become an opt-in package dep (e.g. bucket I/O → bucketbag, #67), never a local import. Recipes = inline; internal tooling may share freely.
  • GPU + output. Check torch.cuda.is_available() and exit clearly if absent. Write to the Hub (push_to_hub) or a bucket (-v hf://…), never bare local paths (Jobs disk is ephemeral; local input dirs mount via -v ./dir:/mnt, synced to a bucket automatically).
  • vLLM image + fail-fast preflight. If the model's arch isn't in a stable vLLM wheel, omit vllm/torch from deps and run on the pinned vllm/vllm-openai image via --image … --python … -e PYTHONPATH=…; add a preflight that sys.exit(1)s naming the exact flags (surya-class — see gotchas). Pinned-image scripts: surya-ocr (:v0.20.1, site-packages), nanonets-ocr2 (:v0.10.2), unlimited-ocr (:unlimited-ocr), deepseek-ocr2/glm-ocr (nightly).
  • Pins are temporary. An image/version pin (:v0.10.2, :v0.20.1, a nightly, surya-ocr==0.20.0, …) is a workaround for a current ecosystem gap — a decode regression, an arch not yet in a stable wheel, a resolver backtrack. In the recipe, record why the pin exists and what would loosen it (e.g. "move back to the default image when a newer vLLM ships a Qwen2.5-VL decode fix"; "drop the nightly once the arch lands in a stable release"). Re-test periodically and relax when the gap closes — that's what the bump-vllm-pins skill is for; prefer floors over exact pins once you can.
  • Env guards on the bare image. Set VLLM_USE_FLASHINFER_SAMPLER=0 (and VLLM_USE_DEEP_GEMM=0 for nightly vLLM) before importing vllm — both JIT paths need nvcc, which the bare uv image lacks.
  • Context-length invariant. --max-tokens ≤ --max-model-len ≤ the model's real max context (config.json max_position_embeddings; VLMs → text_config, mind rope_scaling). vLLM refuses to start if max_model_len is over (we don't set VLLM_ALLOW_LONG_MAX_MODEL_LEN); output can't fit if max_tokens > max_model_len. Check: curl -s https://huggingface.co/<model>/raw/main/config.json | python -c "import json,sys;c=json.load(sys.stdin);t=c.get('text_config',c);print(t.get('max_position_embeddings'),t.get('rope_scaling'))".
  • Output-column collision guard. Every recipe that adds an output column calls ensure_output_columns_free(dataset, [cols], overwrite) (or the inline sink guard for pp-*) so it fails fast instead of duplicating/clobbering an input column; --overwrite opts into replacing it. Default --output-column is markdown (never a bare text). (#66)
  • Bound large images. Full-page recipes cap input pixels / resize, or size max_model_len to fit — a 7–9 MP page is ~14k image tokens. Prefer bounding the input (deterministic) over a giant context; don't auto-size max_model_len from images (it's fixed at engine init, before images are seen).
  • Dep sanity. No stale version caps that drag a transitive lib back — e.g. pyarrow<18 forced an old datasets lacking the Json feature → load_dataset crashed (the glm-ocr bug). Use floors, not ceilings.
  • Error signalling (known gap). Scripts currently write sentinels ([OCR ERROR], [SURYA GENERATE ERROR]) into the output column, so partial failures are silent. A companion ocr_error status column is the deferred fix (see Deferred).

Script status

Legend: ✅ production-ready · ⚠️ works only with a required pinned image · 🧪 experimental/on-hold. "+image" = needs a --image vllm/vllm-openai:<tag> override (not the default uv image).

Script Backend Flavor Note
deepseek-ocr-vllm.py ✅ vLLM (stable) l4x1 NGramPerReqLogitsProcessor anti-repeat
deepseek-ocr2-vllm.py ✅ vLLM (nightly) l4x1 arch needs nightly; addict+matplotlib deps
lighton-ocr2.py ✅ vLLM a100-large / l4x1 resize 1540px; --max-model-len 16384
paddleocr-vl-1.5.py ✅ transformers l4x1 not vLLM (server-only upstream); single-image
paddleocr-vl-1.6.py ✅ vLLM l4x1 smart-resize ~1M px; SOTA OmniDocBench
paddleocr-vl.py ✅ vLLM l4x1
dots-ocr.py ✅ vLLM (stable) l4x1 --max-model-len 32768; no internal resize
dots-ocr-1.5.py ✅ vLLM 0.17.1 l4x1 see gotcha (content_format, mirror, bbox space)
glm-ocr.py ✅ vLLM (nightly) l4x1 VLLM_USE_DEEP_GEMM=0; no pyarrow cap
ovis-ocr2.py ✅ vLLM (stable ≥0.22.1) l4x1 / a10g-small gdn_prefill_backend="triton" (card); card-exact prompt via enable_thinking=False template + llm.generate; <img> region tags filtered by default (--keep-image-tags)
glm-ocr-v2.py 🧪 vLLM (nightly) l4x1 CommitScheduler incremental — on hold (see Deferred)
nanonets-ocr.py ✅ vLLM a10g-small --max-model-len 32768 (--max-tokens 15000)
nanonets-ocr2.py ⚠️+image vLLM :v0.10.2 a10g-small Qwen2.5-VL ≥0.11 regression → pure !
unlimited-ocr-vllm.py ✅+image vLLM :unlimited-ocr l4x1 single-image; multi-page → serve
surya-ocr.py ✅+image vLLM :v0.20.1 l4x1 offline backend inject; site-packages PYTHONPATH
surya-ocr-bucket.py ✅+image vLLM :v0.20.1 l4x1 bucket I/O; pin surya-ocr==0.20.0
lift-extract.py ✅ hf / vLLM a100-large schema-constrained extraction; naming gotcha
nuextract3.py, lfm2-extract.py, lfm2-vl-extract.py ✅ vLLM l4x1 structured extraction
hunyuan-ocr.py ✅ vLLM l4x1 1.0, revision-pinned (root repo became 1.5 in-place); transformers<5.13 cap — see gotcha
hunyuan-ocr-1.5.py ✅ vLLM l4x1 tracks repo root (=1.5); task-locked prompts; transformers<5.13 cap — see gotcha
rolm-ocr.py, smoldocling-ocr.py, numarkdown-ocr.py, qianfan-ocr.py, firered-ocr.py, abot-ocr.py, falcon-ocr.py, olmocr2-vllm.py, dots-mocr.py ✅ vLLM varies see README.md for flags
pp-ocrv6.py, pp-doclayout.py ✅ PaddleOCR / PaddleX l4x1 classical det+rec; dataset or bucket I/O

License note: Surya and lift ship code as Apache-2.0 but weights under a modified OpenRAIL-M (research/personal/<$5M, no competitive use vs Datalab's API) — surfaced in each docstring + card. HunyuanOCR (1.0 + 1.5) is under the Tencent Hunyuan Community License (territory excludes EU/UK/South Korea; standard across Tencent's Hunyuan releases) — surfaced in each docstring + card.

Per-script gotchas

Only the scripts with load-bearing quirks; the rest are unremarkable (README.md covers flags).

surya-ocr.py / surya-ocr-bucket.py — pinned image + site-packages path

Surya-2 (datalab-to/surya-ocr-2, 650M, qwen3_5) needs its known-good vLLM build, and the :v0.20.1 image puts python at /usr/local/bin/python3 and libs at /usr/local/lib/python3.12/site-packages (not the usual dist-packages) — wrong path → No module named 'vllm' → 0/5. It can't Docker-in-Docker Surya's normal server, so it injects an in-process OfflineVLLMBackend into SuryaInferenceManager (subclassing Surya's Backend ABC) and reuses Surya's own prompts/scale_to_fit/HTML+bbox parsing so the offline path matches the server. mm_processor_kwargs={min_pixels:3136, max_pixels:6291456}, max_model_len=18000, logprobs=1 → per-block confidence. Writes two columns (--output-column markdown + surya_blocks JSON). Never name the file surya.py (shadows the package). The recipe now fails fast if vllm isn't importable, naming the required flags. Bucket variant: pin surya-ocr==0.20.0 (loosening it, or adding huggingface-hub>=1.6.0, lets uv backtrack to a surya without surya.inference); copy beats mount for bucket reads (FUSE rglob is ~26× slower on a 38k-file bucket; mount also hit a transient CSI flake); .jp2 via an imagecodecs fallback (Pillow lacks OpenJPEG); resume-by-skip on the output .json.

nanonets-ocr2.py — pinned :v0.10.2 image

Nanonets-OCR2-3B is Qwen2.5-VL, which has a vLLM ≥0.11 decode regression (outputs pure ! on every page) — vllm#27775. 0.9.2/0.10.1/0.10.2 are known-good. Not context length (still ! at 32768) and not torch.compile. Pip-pinning vllm==0.10.2 clashes with modern transformers (old tokenizer API), so run on the :v0.10.2 image (ships a consistent vLLM 0.10.2 + transformers 4.56.1); vllm/torch omitted from deps. --max-model-len 32768 (the 15000 --max-tokens can't fit 8192). Re-test the default image when a newer vLLM ships a Qwen2.5-VL decode fix.

dots-ocr-1.5.py — content_format="string" + resized-bbox space

Must pass chat_template_content_format="string" to llm.chat() — the model's tokenizer_config.json template expects string content; without it you get ~1 token then EOS (empty output). The v1.5 weights aren't on HF from the authors — mirrored to davanstrien/dots.ocr-1.5 from ModelScope (MIT-based). Layout bboxes are in the resized image space (Qwen2VLImageProcessor.smart_resize, max_pixels=11,289,600, factor=28); map back with:

import math
def smart_resize(h, w, factor=28, min_pixels=3136, max_pixels=11289600):
    h_bar, w_bar = max(factor, round(h/factor)*factor), max(factor, round(w/factor)*factor)
    if h_bar*w_bar > max_pixels:
        beta = math.sqrt((h*w)/max_pixels); h_bar, w_bar = math.floor(h/beta/factor)*factor, math.floor(w/beta/factor)*factor
    elif h_bar*w_bar < min_pixels:
        beta = math.sqrt(min_pixels/(h*w)); h_bar, w_bar = math.ceil(h*beta/factor)*factor, math.ceil(w*beta/factor)*factor
    return h_bar, w_bar
# orig_x = bbox_x * (orig_w / w_bar);  orig_y = bbox_y * (orig_h / h_bar)

(Same smart_resize/max_pixels=11.29M applies to dots-ocr.py v1's processor cap — but the content_format="string" fix does not: v1 works with the auto-detected openai chat format.)

unlimited-ocr-vllm.py — dedicated image, single-image batch

Baidu baidu/Unlimited-OCR (3.3B, DeepSeek-OCR descendant); arch is in no stable vLLM wheel, so it runs on vllm/vllm-openai:unlimited-ocr (:unlimited-ocr-cu129 on Hopper), standard /usr/bin/python3

  • dist-packages. NGramPerReqLogitsProcessor (re-exported via unlimited_ocr), prompt <image>document parsing., limit_mm_per_prompt={"image":1}, --strip-grounding drops <|det|>/<|ref|>. Batch recipe stays single-image; multi-page is finicky offline (one <image> per page; degrades on hard scans) and belongs to serving — both engines read clean multi-page docs, but SGLang is more robust on hard/degraded scans. Serving setup (SGLang pin lmsysorg/sglang:v0.5.10.post1, a100+flashinfer — HF h200 nodes fail with CUDA error 802) is in serving-unlimited-ocr.md.

lift-extract.py — naming + backends

Datalab lift (9B, Qwen3.5), schema-constrained image/PDF → JSON; the only recipe ingesting PDFs directly. Must not be named lift.py (shadows the installed lift package → ImportError). Two in-process backends via --method: hf (default image, plain model.generate) and vllm (needs the vllm/vllm-openai image; reproduces lift's own recipe: mm_processor_kwargs={min_pixels:3136,max_pixels:861696}, guided JSON schema, temperature=0.0,top_p=0.1,max_tokens=12384). Pin --model datalab-to/lift via the MODEL_CHECKPOINT env (settings read env at import).

deepseek-ocr-vllm.py / deepseek-ocr2-vllm.py

v1 uses the official offline pattern (llm.generate() + NGramPerReqLogitsProcessor for repetition). Known bug (hit on vLLM nightly, 2026-02-12; unverified on the stable wheels v1 now resolves): some aspect ratios trip images_crop dim[2] expected 1024, got 640 (gundam-mode default vs a validator expecting 1024²) — hit 2/10 on ufo-ColPali, aspect-ratio dependent, no upstream issue filed (vllm#28160 is the related request). v2 needs nightly vLLM (DeepseekOCR2ForCausalLM not in stable) + addict/matplotlib (its HF custom code), plus limit_mm_per_prompt={"image":1}.

hunyuan-ocr.py / hunyuan-ocr-1.5.py — upstream replaced the repo root in-place

On 2026-07-06 Tencent pushed HunyuanOCR-1.5 into the same repo tencent/HunyuanOCR (1.5 at root, 1.0 archived under v1.0/, DFlash draft under dflash/, no 1.0 tag or branch). vLLM can't load a repo subfolder, so hunyuan-ocr.py pins the last 1.0 commit by revision (f6af82ee…) — that pin is the 1.0 identity, never loosen it to main; 1.5 is its own recipe (hunyuan-ocr-1.5.py, tracks root, has --revision as insurance against the next in-place swap). Separate breakage, both scripts: stable vLLM ≤0.24.0's hunyuan_vl_image.py does a string-key AutoImageProcessor.register(...) which transformers 5.13 rejects ('str' object has no attribute '__module__' → "architectures failed to be inspected" at engine init) — hence the transformers<5.13 cap in both; drop it when the vllm#47872 fix ships in a stable wheel. 1.5 prompts are task-locked (12 types, Chinese wording, from the official client's hunyuan_tasks.py) and sampling is card-locked (temp 0.0, rep-penalty 1.08) — don't "improve" either; upstream observed hand-tweaked prompts silently degrade quality.

glm-ocr.py

Chatty on blank pages / can emit degenerate repeats — that's model quality, not a crash; don't re-debug it as a recipe bug. (The actual historical crash was the pyarrow<18 cap — see Conventions.)

lighton-ocr2.py

The original breakage was not vLLM — it was a dead HF_HUB_ENABLE_HF_TRANSFER=1 (the hf_transfer package is gone), which surfaced as "Can't load image processor". Removed. Pixtral ViT + Qwen3, RLVR-trained, resize 1540px @200 DPI, --max-model-len 16384. paddleocr-vl-1.5.py uses the transformers backend (single-image) because PaddleOCR-VL only supports vLLM in server mode.

Internal tooling

Not user recipes — benchmark/eval infra. Result tables + validation history → OCR-BENCHMARK.md.

ocr-bench-run.py — coordinator

Launches N OCR models on the same dataset, each pushing to a shared repo as a separate config via --config/--create-pr. Eval separately with ocr-vllm-judge.py / ocr-elo-bench.py. Registry (4 models): glm-ocr, deepseek-ocr (auto --prompt-mode free), lighton-ocr-2, dots-ocr; each has a default_args.

uv run ocr-bench-run.py source-dataset --output my-bench --max-samples 50
uv run ocr-bench-run.py --list-models              # registry table
uv run ocr-bench-run.py ... --models glm-ocr dots-ocr --dry-run
uv run ocr-elo-bench.py my-bench --from-prs --mode both   # eval from PRs, no merge

ocr-vllm-judge.py — offline vLLM jury judge

Pairwise OCR-quality comparisons via vLLM offline LLM(); jury mode (multiple models vote, majority aggregation) with 0 parse failures (structured output via the StructuredOutputsParams → GuidedDecodingParams → prompt shim). Prefer over the API judge (ocr-jury-bench.py) for batch eval — no rate limits, reproducible. --from-prs loads configs from open PRs without merging; --save-results REPO persists comparisons/leaderboard/metadata. A100 recommended for jury mode; L4 works for a single 7B judge.

hf jobs uv run --flavor a100-large -s HF_TOKEN ocr-vllm-judge.py davanstrien/ocr-bench-nls-50 --from-prs \
    --judge-model Qwen/Qwen2.5-VL-7B-Instruct --judge-model Qwen/Qwen3-VL-8B-Instruct --max-samples 50

ocr-human-eval.py — blind human A/B

Gradio app for blind A/B with ELO + optional agreement-vs-judge analysis (split-jury comparisons shown first; round-robin image variety). Resume-safe (atomic JSON per vote).

uv run ocr-human-eval.py davanstrien/ocr-bench-rubenstein --from-prs \
    --judge-results davanstrien/ocr-bench-rubenstein-judge --max-samples 5

Validation headline (full tables in OCR-BENCHMARK.md): DeepSeek-OCR is consistently #1 across datasets and eval methods; middle-pack rankings are dataset-dependent; a jury of small models gives 0 parse failures; Kimi K2.5 (170B) is the only judge matching the human's #1 (small judges overrate LightOnOCR-2's commentary style).

Deferred / tracked

  • bucketbag adoption — evaluate adopting bucketbag for the bucket recipes (slim recipes / harden bucketbag / find other beneficiaries) → #67.
  • Self-review skill (spark) — a dev-only skill (sibling to bump-vllm-pins) that reviews a recipe/diff against the Conventions block: context-length, collision guard, vLLM-image + preflight, env guards, dep sanity, image bounding, optional Jobs smoke. Enforces that list; build after the current work.
  • Error-signalling — companion ocr_error status column (null cell + truncated exception) instead of sentinels in the output column, so "read nothing" ≠ "run errored". Touches ~all recipes; deferred.
  • OCR smoke-test dataset — a tiny curated set (~20–30 images across doc-type/quality/language/layout, ground truth where possible) for fast CI-style regression checks after dep bumps. Pairs with the skill.
  • Multi-page batch (Unlimited-OCR) — an SGLang-server-in-job recipe for robust multi-page at scale (single-image vLLM stays the batch default). Gated on a real corpus-scale need + the h200/fa3 infra fix; see serving-unlimited-ocr.md.
  • ALTO XML export — from surya_blocks (block-level bbox→HPOS/VPOS/…, label→TextBlock/Illustration); the surya-ocr-bucket test bucket ships CA's own ALTO .xml as a diff target.
  • Incremental uploads — superseded by HF Buckets / bucketbag (#67); glm-ocr-v2.py keeps the older CommitScheduler resume path for very large jobs today (do not port it — on hold).
  • Leaderboard Space — public ELO/pointwise view fed by the benchmark datasets. Idea only.
  • MonkeyOCRv2 — evaluated, not added (2026-07-14). zenosai/MonkeyOCRv2-S-Parsing (0.6B) needs the GitHub repo's custom multi-stage pipeline (parsing/parse.py, structure detection) — not pip-packaged, pins vllm==0.11.2, and the card text says "academic research and non-commercial use only" while the metadata tag says apache-2.0 (conflicting license signals). Revisit if it gets packaged / vLLM-native support or the license is clarified.

Watch: deepseek-ocr2 / glm-ocr stay on nightly vLLM until their arch lands in a stable release. The nightly index (https://wheels.vllm.ai/nightly) occasionally has transient build issues (e.g. only ARM wheels) — if a nightly-recipe install fails on resolution, wait and retry before debugging the recipe.

Change log

  • 2026-07-29 — added the first two -saturate.py companions: lighton-ocr2-saturate.py and ovis-ocr2-saturate.py. Same model/prompt/sampling/post-processing as their -server.py siblings; the driver half (concurrency, retries, output, resume) is the saturate package (pinned >=0.1.1, the allowed package-dep pattern — no local imports). Each carries a SERVING dict at the top (serve flags + client sampling + context-math assert) as the machine-readable tuning prior — the runtime-consumed successor to the dropped [tool.serving] header idea. Output shape differs from -server.py: a NEW dataset repo of {id, markdown, …, error} parquet rows keyed by input id (resume = anti-join on id), not input+column push; failed pages become durable error rows (--retry-errors heals), NOT [OCR ERROR] sentinels — the first recipes closing the error-signalling gap noted in Conventions.
  • 2026-07-14 — added ovis-ocr2.py (ATH-MaaS/OvisOCR2, 0.9B Qwen3.5, 96.58 OmniDocBench v1.6, Apache-2.0; stable vLLM ≥0.22.1, gdn_prefill_backend="triton", card-exact prompt/postprocessing). Smoke-tested green on the default uv image, a10g-small, resolved vLLM 0.25.1 (5/5 pages, tags filtered, stamp OK). Evaluated MonkeyOCRv2 alongside it — deferred (see Deferred / tracked).
  • 2026-07-08 — HunyuanOCR upstream repo swap: pinned hunyuan-ocr.py to the last 1.0 revision + added hunyuan-ocr-1.5.py (12 task types, locked sampling); transformers<5.13 cap in both for the stable-vLLM HunyuanVL register breakage (vllm#47872). See the hunyuan gotcha above.
  • 2026-07-01 — large full-page scan fixes (#65): surya vLLM-missing preflight; dots 8192→32768 + --max-pixels; lighton-ocr2 8192→16384; glm dropped pyarrow<18 (→ datasets Json load crash) + VLLM_USE_DEEP_GEMM=0 + --max-pixels; pp-ocrv6 --output-column + collision guard. Then the output-column collision-guard + --overwrite sweep across the recipes (#66); nanonets-ocr 8192→32768.
  • Earlier — per-script fixes are recorded in git history + the gotchas above; benchmark runs in OCR-BENCHMARK.md.