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README.md

@@ -5,11 +5,11 @@ tags: [uv-script, ocr, vision-language-model, document-processing, hf-jobs]
 
 # OCR UV Scripts
 
-> Part of [uv-scripts](https://huggingface.co/uv-scripts) - ready-to-run ML tools powered by UV and HuggingFace Jobs.
+> Part of [uv-scripts](https://huggingface.co/uv-scripts) — self-contained UV scripts you run on Hugging Face Jobs in one command.
 
-21 OCR scripts (text extraction) + 1 layout-detection script. Pick a model, point at your dataset, get markdown — no setup required. Layout-detection runs separately when you need bboxes for regions (text/title/table/figure/...) rather than the text itself.
+A model zoo of OCR scripts — one per model — that add a `markdown` column to an image dataset. Pick a model from the table below, point it at your dataset, and run it on a GPU with one command. Two companions sit alongside: `pp-doclayout.py` detects layout regions (bboxes for text/title/table/figure/…) instead of text, and `ocr-vllm-judge.py` compares model outputs head-to-head.
 
-## 🚀 Quick Start
+## Quick Start
 
 Run OCR on any dataset without needing your own GPU:
 
@@ -22,14 +22,24 @@ hf jobs uv run --flavor l4x1 \
     --max-samples 10
 ```
 
-That's it! The script will:
+This will:
 
-- Process first 10 images from your dataset
+- Process the first 10 images from your dataset
 - Add OCR results as a new `markdown` column
 - Push the results to a new dataset
 - View results at: `https://huggingface.co/datasets/[your-output-dataset]`
 
-<details><summary>All scripts at a glance (sorted by model size)</summary>
+## Models at a glance
+
+**Start here:** for a quick first run, try **`lighton-ocr2.py`** (1B, very fast) or **`paddleocr-vl-1.6.py`** (0.9B, current OmniDocBench SOTA); for the smallest footprint, **`falcon-ocr.py`** (0.3B, strong on tables). Reach for a 7–8B model only when quality demands it. Several of these models sit on the public [olmOCR-Bench](https://huggingface.co/datasets/allenai/olmOCR-bench) — pull the live ranking from your terminal in one command:
+
+```bash
+hf datasets leaderboard allenai/olmOCR-bench
+```
+
+But which model wins on *your* documents is still document-dependent — so [ocr-bench](https://github.com/davanstrien/ocr-bench) builds a **per-collection leaderboard** for your own data (pairwise VLM-as-judge, optionally human-validated), using these scripts under the hood.
+
+_Sorted by model size:_
 
 | Script | Model | Size | Backend | Notes |
 |--------|-------|------|---------|-------|
@@ -38,6 +48,7 @@ That's it! The script will:
 | `glm-ocr.py` | [GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) | 0.9B | vLLM | 94.62% OmniDocBench V1.5 |
 | `paddleocr-vl.py` | [PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) | 0.9B | Transformers | 4 task modes (ocr/table/formula/chart) |
 | `paddleocr-vl-1.5.py` | [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) | 0.9B | Transformers | 94.5% OmniDocBench, 6 task modes |
+| `paddleocr-vl-1.6.py` | [PaddleOCR-VL-1.6](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6) | 0.9B | vLLM | **96.33% OmniDocBench v1.6** (SOTA), drop-in upgrade of 1.5 |
 | `lighton-ocr.py` | [LightOnOCR-1B](https://huggingface.co/lightonai/LightOnOCR-1B-1025) | 1B | vLLM | Fast, 3 vocab sizes |
 | `lighton-ocr2.py` | [LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B) | 1B | vLLM | 7× faster than v1, RLVR trained |
 | `hunyuan-ocr.py` | [HunyuanOCR](https://huggingface.co/tencent/HunyuanOCR) | 1B | vLLM | Lightweight VLM |
@@ -56,7 +67,7 @@ That's it! The script will:
 | `rolm-ocr.py` | [RolmOCR](https://huggingface.co/reducto/RolmOCR) | 7B | vLLM | Qwen2.5-VL based, general-purpose |
 | `numarkdown-ocr.py` | [NuMarkdown-8B](https://huggingface.co/numind/NuMarkdown-8B-Thinking) | 8B | vLLM | Reasoning-based OCR |
 
-</details>
+**Variants & tools** (same models, different I/O): `glm-ocr-v2.py` adds checkpoint/resume for very large jobs · `glm-ocr-bucket.py` and `falcon-ocr-bucket.py` read images/PDFs from a mounted bucket and write one `.md` per page · `ocr-vllm-judge.py` runs pairwise OCR-quality comparisons.
 
 ## Layout detection (not OCR)
 
@@ -76,7 +87,7 @@ Source/sink can be either an HF dataset repo OR an `hf://buckets/...` URL (auto-
 
 ## Common Options
 
-All scripts accept the same core flags. Model-specific defaults (batch size, context length, temperature) are tuned per model based on model card recommendations and can be overridden.
+The scripts aim to expose a **consistent interface**: every OCR model script takes `input-dataset output-dataset` as positional arguments, accepts the shared core flags below, and writes a `markdown` column — so switching models is usually just swapping the script URL. Models differ where they need to, though: some add their own flags (task modes, resolution presets, `--think`, vocab sizes), a few need a specific Docker image, and per-model defaults (batch size, context length, temperature) are tuned to each model card. Always check a script's `--help` for its specifics.
 
 | Option | Description |
 |--------|-------------|
@@ -101,28 +112,7 @@ Every script supports `--help` to see all available options:
 uv run glm-ocr.py --help
 ```
 
-## Example: GLM-OCR
-
-[GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) (0.9B) scores 94.62% on OmniDocBench V1.5 and supports OCR, formula, and table extraction:
-
-```bash
-# Basic OCR
-hf jobs uv run --flavor l4x1 -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    my-documents my-ocr-output
-
-# Table extraction
-hf jobs uv run --flavor l4x1 -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    my-documents my-tables --task table
-
-# Test on 10 samples first
-hf jobs uv run --flavor l4x1 -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    my-documents my-test --max-samples 10
-```
-
-## Example: NuExtract3 (markdown OCR **+ structured extraction**)
+## NuExtract3: markdown OCR + structured extraction
 
 [NuExtract3](https://huggingface.co/numind/NuExtract3) (4B, Apache-2.0) is the one script here that does both document-to-markdown OCR *and* schema-guided JSON extraction. Give it a template (or a JSON Schema / Pydantic model) and it returns JSON shaped to match.
 
@@ -149,618 +139,61 @@ hf jobs uv run --flavor a100-large \
     --template '{"store": "verbatim-string", "date": "date", "total": "number"}'
 ```
 
-**Templates** (`--template`) and **JSON Schemas** (`--schema`) each accept **inline JSON, a URL, or a file path**. So a schema can be hosted once and reused:
-
-```bash
-# From a URL (e.g. an HF dataset's raw file)
-... nuextract3.py docs out --template https://huggingface.co/datasets/ORG/REPO/raw/main/card.json
-
-# From a JSON Schema / Pydantic model — Model.model_json_schema() dumped to JSON,
-# auto-converted via numind's convert_json_schema_to_nuextract_template
-... nuextract3.py docs out --schema invoice-schema.json
-
-# From a mounted bucket (host configs in a bucket, mount read-only)
-hf jobs uv run --flavor a100-large \
-    --image vllm/vllm-openai:latest --python /usr/bin/python3 \
-    -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages -s HF_TOKEN \
-    -v hf://buckets/USER/configs:/configs:ro \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \
-    docs out --template /configs/card.json
-```
-
-Add `--enable-thinking` for harder layouts (slower; reasoning trace stored in a `<output-column>_reasoning` column). Template field names act as the model's extraction instructions, so name them descriptively — but note that overly leading names can prompt over-generation, so verify against a few examples.
-
-<details><summary>Detailed per-model documentation</summary>
-
-### PaddleOCR-VL-1.5 (`paddleocr-vl-1.5.py`) — 6 task modes
-
-OCR using [PaddlePaddle/PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) with 94.5% accuracy:
-
-- **94.5% on OmniDocBench v1.5** (0.9B parameters)
-- 🧩 **Ultra-compact** - Only 0.9B parameters
-- 📝 **OCR mode** - General text extraction to markdown
-- 📊 **Table mode** - HTML table recognition
-- 📐 **Formula mode** - LaTeX mathematical notation
-- 📈 **Chart mode** - Chart and diagram analysis
-- 🔍 **Spotting mode** - Text spotting with localization (higher resolution)
-- 🔖 **Seal mode** - Seal and stamp recognition
-- 🌍 **Multilingual** - Support for multiple languages
-
-**Task Modes:**
-
-- `ocr`: General text extraction (default)
-- `table`: Table extraction to HTML
-- `formula`: Mathematical formula to LaTeX
-- `chart`: Chart and diagram analysis
-- `spotting`: Text spotting with localization
-- `seal`: Seal and stamp recognition
-
-**Quick start:**
-
-```bash
-# Basic OCR mode
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-
-# Table extraction
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
-    documents tables-extracted \
-    --task-mode table
-
-# Seal recognition
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
-    documents seals-extracted \
-    --task-mode seal
-```
-
-### PaddleOCR-VL (`paddleocr-vl.py`) 🎯 Smallest model with task-specific modes!
-
-Ultra-compact OCR using [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) with only 0.9B parameters:
-
-- 🎯 **Smallest model** - Only 0.9B parameters (even smaller than LightOnOCR!)
-- 📝 **OCR mode** - General text extraction to markdown
-- 📊 **Table mode** - HTML table recognition and extraction
-- 📐 **Formula mode** - LaTeX mathematical notation
-- 📈 **Chart mode** - Structured chart and diagram analysis
-- 🌍 **Multilingual** - Support for multiple languages
-- ⚡ **Fast initialization** - Tiny model size for quick startup
-- 🔧 **ERNIE-4.5 based** - Different architecture from Qwen models
-
-**Task Modes:**
-
-- `ocr`: General text extraction (default)
-- `table`: Table extraction to HTML
-- `formula`: Mathematical formula to LaTeX
-- `chart`: Chart and diagram analysis
-
-**Quick start:**
-
-```bash
-# Basic OCR mode
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-
-# Table extraction
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
-    documents tables-extracted \
-    --task-mode table \
-    --batch-size 32
-```
-
-### GLM-OCR (`glm-ocr.py`) 🏆 SOTA on OmniDocBench V1.5!
-
-Compact high-performance OCR using [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) with 0.9B parameters:
-
-- 🏆 **94.62% on OmniDocBench V1.5** - #1 overall ranking
-- 🧠 **Multi-Token Prediction** - MTP loss + stable full-task RL for quality
-- 📝 **Text recognition** - Clean markdown output
-- 📐 **Formula recognition** - LaTeX mathematical notation
-- 📊 **Table recognition** - Structured table extraction
-- 🌍 **Multilingual** - zh, en, fr, es, ru, de, ja, ko
-- ⚡ **Compact** - Only 0.9B parameters, MIT licensed
-- 🔧 **CogViT + GLM** - Visual encoder with efficient token downsampling
-
-**Task Modes:**
-
-- `ocr`: Text recognition (default)
-- `formula`: LaTeX formula recognition
-- `table`: Table extraction
-
-**Quick start:**
-
-```bash
-# Basic OCR
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-
-# Formula recognition
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    scientific-papers formulas-extracted \
-    --task formula
-
-# Table extraction
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    documents tables-extracted \
-    --task table
-```
-
-### LightOnOCR (`lighton-ocr.py`) ⚡ Good one to test first since it's small and fast!
-
-Fast and compact OCR using [lightonai/LightOnOCR-1B-1025](https://huggingface.co/lightonai/LightOnOCR-1B-1025):
-
-- ⚡ **Fastest**: 5.71 pages/sec on H100, ~6.25 images/sec on A100 with batch_size=4096
-- 🎯 **Compact**: Only 1B parameters - quick to download and initialize
-- 🌍 **Multilingual**: 3 vocabulary sizes for different use cases
-- 📐 **LaTeX formulas**: Mathematical notation in LaTeX format
-- 📊 **Table extraction**: Markdown table format
-- 📝 **Document structure**: Preserves hierarchy and layout
-- 🚀 **Production-ready**: 76.1% benchmark score, used in production
-
-**Vocabulary sizes:**
-
-- `151k`: Full vocabulary, all languages (default)
-- `32k`: European languages, ~12% faster decoding
-- `16k`: European languages, ~12% faster decoding
-
-**Quick start:**
-
-```bash
-# Test on 100 samples with English text (32k vocab is fastest for European languages)
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \
-    your-input-dataset your-output-dataset \
-    --vocab-size 32k \
-    --batch-size 32 \
-    --max-samples 100
-
-# Full production run on A100 (can handle huge batches!)
-hf jobs uv run --flavor a100-large \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \
-    your-input-dataset your-output-dataset \
-    --vocab-size 32k \
-    --batch-size 4096 \
-    --temperature 0.0
-```
-
-### LightOnOCR-2 (`lighton-ocr2.py`) ⚡ Fastest OCR model!
-
-Next-generation fast OCR using [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B) with RLVR training:
-
-- ⚡ **7× faster than v1**: 42.8 pages/sec on H100 (vs 5.71 for v1)
-- 🎯 **Higher accuracy**: 83.2% on OlmOCR-Bench (+7.1% vs v1)
-- 🧠 **RLVR trained**: Eliminates repetition loops and formatting errors
-- 📚 **Better dataset**: 2.5× larger training data with cleaner annotations
-- 🌍 **Multilingual**: Optimized for European languages
-- 📐 **LaTeX formulas**: Mathematical notation support
-- 📊 **Table extraction**: Markdown table format
-- 💪 **Production-ready**: Outperforms models 9× larger
-
-**Quick start:**
-
-```bash
-# Test on 100 samples
-hf jobs uv run --flavor a100-large \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
-    your-input-dataset your-output-dataset \
-    --batch-size 32 \
-    --max-samples 100
-
-# Full production run
-hf jobs uv run --flavor a100-large \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
-    your-input-dataset your-output-dataset \
-    --batch-size 32
-```
-
-### DeepSeek-OCR (`deepseek-ocr-vllm.py`)
-
-Advanced document OCR using [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) with visual-text compression:
-
-- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format
-- 📊 **Tables** - Extracted as HTML/markdown
-- 📝 **Document structure** - Headers, lists, formatting preserved
-- 🖼️ **Image grounding** - Spatial layout with bounding boxes
-- 🔍 **Complex layouts** - Multi-column and hierarchical structures
-- 🌍 **Multilingual** - Multiple language support
-- 🎚️ **Resolution modes** - 5 presets for speed/quality trade-offs
-- 💬 **Prompt modes** - 5 presets for different OCR tasks
-- ⚡ **Fast batch processing** - vLLM acceleration
-
-**Resolution Modes:**
-
-- `tiny` (512×512): Fast, 64 vision tokens
-- `small` (640×640): Balanced, 100 vision tokens
-- `base` (1024×1024): High quality, 256 vision tokens
-- `large` (1280×1280): Maximum quality, 400 vision tokens
-- `gundam` (dynamic): Adaptive multi-tile (default)
-
-**Prompt Modes:**
-
-- `document`: Convert to markdown with grounding (default)
-- `image`: OCR any image with grounding
-- `free`: Fast OCR without layout
-- `figure`: Parse figures from documents
-- `describe`: Detailed image descriptions
-
-### RolmOCR (`rolm-ocr.py`)
-
-Fast general-purpose OCR using [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR) based on Qwen2.5-VL-7B:
-
-- 🚀 **Fast extraction** - Optimized for speed and efficiency
-- 📄 **Plain text output** - Clean, natural text representation
-- 💪 **General-purpose** - Works well on various document types
-- 🔥 **Large context** - Handles up to 16K tokens
-- ⚡ **Batch optimized** - Efficient processing with vLLM
-
-### Nanonets OCR (`nanonets-ocr.py`)
-
-State-of-the-art document OCR using [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) that handles:
-
-- 📐 **LaTeX equations** - Mathematical formulas preserved
-- 📊 **Tables** - Extracted as HTML format
-- 📝 **Document structure** - Headers, lists, formatting maintained
-- 🖼️ **Images** - Captions and descriptions included
-- ☑️ **Forms** - Checkboxes rendered as ☐/☑
-
-### Nanonets OCR2 (`nanonets-ocr2.py`)
+**Templates** (`--template`) and **JSON Schemas** (`--schema`) each accept **inline JSON, a URL, or a file path**, so a schema can be hosted once and reused. Add `--enable-thinking` for harder layouts (slower; reasoning trace stored in a `<output-column>_reasoning` column). Template field names act as the model's extraction instructions, so name them descriptively — overly leading names can prompt over-generation, so verify against a few examples.
 
-Next-generation Nanonets OCR using [nanonets/Nanonets-OCR2-3B](https://huggingface.co/nanonets/Nanonets-OCR2-3B) with improved accuracy:
+## Model-specific modes & flags
 
-- 🎯 **Enhanced quality** - 3.75B parameters for superior OCR accuracy
-- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
-- 📊 **Advanced tables** - Improved HTML table extraction
-- 📝 **Document structure** - Headers, lists, formatting maintained
-- 🖼️ **Smart image captions** - Intelligent descriptions and captions
-- ☑️ **Forms** - Checkboxes rendered as ☐/☑
-- 🌍 **Multilingual** - Enhanced language support
-- 🔧 **Based on Qwen2.5-VL** - Built on state-of-the-art vision-language model
+Beyond the shared flags, some models add their own. Run `--help` on any script for the full list; the common ones:
 
-### SmolDocling (`smoldocling-ocr.py`)
+| Script | Extra options |
+|--------|---------------|
+| `glm-ocr.py` | `--task ocr\|formula\|table` |
+| `paddleocr-vl.py` | `--task-mode ocr\|table\|formula\|chart` |
+| `paddleocr-vl-1.5.py` | `--task-mode ocr\|table\|formula\|chart\|spotting\|seal` |
+| `paddleocr-vl-1.6.py` | `--task-mode ocr\|table\|formula` |
+| `lighton-ocr.py` | `--vocab-size 151k\|32k\|16k` (smaller = faster on European languages) |
+| `deepseek-ocr-vllm.py` | `--resolution-mode tiny\|small\|base\|large\|gundam`, `--prompt-mode document\|image\|free\|figure\|describe`; pass `-e UV_TORCH_BACKEND=auto` |
+| `dots-ocr.py` | `--prompt-mode ocr\|layout-all\|layout-only` |
+| `dots-mocr.py` | `--prompt-mode` (8: ocr, layout-all, layout-only, web-parsing, scene-spotting, grounding-ocr, svg, general); SVG: `--model rednote-hilab/dots.mocr-svg --prompt-mode svg` |
+| `qianfan-ocr.py` | `--prompt-mode ocr\|table\|formula\|chart\|scene\|kie`, `--think` (Layout-as-Thought); `kie` needs `--custom-prompt` |
+| `numarkdown-ocr.py` | `--include-thinking` (store the reasoning trace) |
+| `nuextract3.py` | `--template` / `--schema` / `--enable-thinking` — see the NuExtract3 section above |
 
-Ultra-compact document understanding using [ds4sd/SmolDocling-256M-preview](https://huggingface.co/ds4sd/SmolDocling-256M-preview) with only 256M parameters:
+**Image-mode models** — `abot-ocr.py` and `nuextract3.py` (Qwen3.5 architecture) need the `vllm/vllm-openai` image because the default uv-script image lacks `nvcc`. Add `--image vllm/vllm-openai:latest --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages` (see the NuExtract3 example above for the full command).
 
-- 🏷️ **DocTags format** - Efficient XML-like representation
-- 💻 **Code blocks** - Preserves indentation and syntax
-- 🔢 **Formulas** - Mathematical expressions with layout
-- 📊 **Tables & charts** - Structured data extraction
-- 📐 **Layout preservation** - Bounding boxes and spatial info
-- ⚡ **Ultra-fast** - Tiny model size for quick inference
+## Output & features
 
-### NuMarkdown (`numarkdown-ocr.py`)
+- **Markdown column** — each run adds an `--output-column` (default `markdown`) with the OCR result.
+- **Multi-model comparison** — every script records `inference_info`, so you can run several models into the *same* dataset and compare. Point a second model at the same output repo:
+  ```bash
+  uv run rolm-ocr.py     my-dataset my-dataset --max-samples 100
+  uv run nanonets-ocr.py my-dataset my-dataset --max-samples 100   # appends
+  ```
+- **Reproducible sampling** — `--shuffle` (with `--seed`, default 42) draws a representative sample instead of the first N rows.
+- **Automatic dataset cards** — every run writes a card with the model config, processing stats, column descriptions, and a reproduction command.
 
-Advanced reasoning-based OCR using [numind/NuMarkdown-8B-Thinking](https://huggingface.co/numind/NuMarkdown-8B-Thinking) that analyzes documents before converting to markdown:
-
-- 🧠 **Reasoning Process** - Thinks through document layout before generation
-- 📊 **Complex Tables** - Superior table extraction and formatting
-- 📐 **Mathematical Formulas** - Accurate LaTeX/math notation preservation
-- 🔍 **Multi-column Layouts** - Handles complex document structures
-- ✨ **Thinking Traces** - Optional inclusion of reasoning process with `--include-thinking`
-
-### dots.mocr (`dots-mocr.py`) — SVG generation + SOTA OCR
-
-Advanced multilingual OCR and SVG generation using [rednote-hilab/dots.mocr](https://huggingface.co/rednote-hilab/dots.mocr) with 3B parameters:
-
-- 🌍 **100+ Languages** - Extensive multilingual support
-- 📝 **Document OCR** - Clean text extraction (default mode)
-- 📊 **Layout Analysis** - Structured output with bboxes and categories
-- 📐 **Formula recognition** - LaTeX format support
-- 🖼️ **SVG generation** - Convert charts, UI layouts, figures to editable SVG code
-- 🔀 **8 prompt modes** - OCR, layout-all, layout-only, web-parsing, scene-spotting, grounding-ocr, svg, general
-- 📄 **[Paper](https://arxiv.org/abs/2603.13032)** - 83.9% on olmOCR-Bench
-
-**SVG variant:** Use `--model rednote-hilab/dots.mocr-svg` with `--prompt-mode svg` for best SVG results.
-
-**Quick start:**
+## More examples
 
 ```bash
-# Basic OCR
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-
-# SVG generation from charts/figures
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
-    your-charts svg-output \
-    --prompt-mode svg --model rednote-hilab/dots.mocr-svg
+# DeepSeek-OCR on historical scans, large resolution mode
+hf jobs uv run --flavor a100-large -s HF_TOKEN -e UV_TORCH_BACKEND=auto \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
+    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset out \
+    --max-samples 100 --shuffle --resolution-mode large
 
-# Layout analysis with bounding boxes
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
+# dots.mocr — SVG generation from charts/figures
+hf jobs uv run --flavor l4x1 -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
-    your-documents layout-output \
-    --prompt-mode layout-all
-```
-
-### DoTS.ocr v1 (`dots-ocr.py`)
-
-Compact multilingual OCR using [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) with only 1.7B parameters:
-
-- 🌍 **100+ Languages** - Extensive multilingual support
-- 📝 **Simple OCR** - Clean text extraction (default mode)
-- 📊 **Layout Analysis** - Optional structured output with bboxes and categories
-- 📐 **Formula recognition** - LaTeX format support
-- 🎯 **Compact** - Only 1.7B parameters, efficient on smaller GPUs
-- 🔀 **Flexible prompts** - Switch between OCR, layout-all, and layout-only modes
-
-### FireRed-OCR (`firered-ocr.py`)
-
-Document OCR using [FireRedTeam/FireRed-OCR](https://huggingface.co/FireRedTeam/FireRed-OCR), a 2.1B model fine-tuned from Qwen3-VL-2B-Instruct:
-
-- 📝 **Structured Markdown** - Preserves headings, paragraphs, lists
-- 📐 **LaTeX formulas** - Inline and block math support
-- 📊 **HTML tables** - Table extraction with `<table>` tags
-- 🪶 **Lightweight** - 2.1B parameters, runs on L4 GPU
-- 📜 **Apache 2.0** - Permissive license
-
-**Quick start:**
-
-```bash
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/firered-ocr.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-```
+    your-charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg
 
-### Qianfan-OCR (`qianfan-ocr.py`) — #1 on OmniDocBench v1.5
-
-End-to-end document intelligence using [baidu/Qianfan-OCR](https://huggingface.co/baidu/Qianfan-OCR) with 4.7B parameters:
-
-- **93.12 on OmniDocBench v1.5** — #1 end-to-end model
-- **79.8 on OlmOCR Bench** — #1 end-to-end model
-- 🧠 **Layout-as-Thought** — Optional reasoning phase for complex layouts (`--think`)
-- 🌍 **192 languages** — Latin, CJK, Arabic, Cyrillic, and more
-- 📝 **OCR mode** — Document parsing to markdown (default)
-- 📊 **Table mode** — HTML table extraction
-- 📐 **Formula mode** — LaTeX recognition
-- 📈 **Chart mode** — Chart understanding and analysis
-- 🔍 **Scene mode** — Scene text extraction
-- 🔑 **KIE mode** — Key information extraction with custom prompts
-
-**Prompt Modes:**
-
-- `ocr`: Document parsing to markdown (default)
-- `table`: Table extraction to HTML
-- `formula`: Formula recognition to LaTeX
-- `chart`: Chart understanding
-- `scene`: Scene text extraction
-- `kie`: Key information extraction (requires `--custom-prompt`)
-
-**Quick start:**
-
-```bash
-# Basic OCR
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 100
-
-# Layout-as-Thought for complex documents
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
-    your-input-dataset your-output-dataset \
-    --think --max-samples 50
-
-# Key information extraction
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
+# Qianfan — key-information extraction
+hf jobs uv run --flavor l4x1 -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
     invoices extracted-fields \
     --prompt-mode kie --custom-prompt "Extract: name, date, total. Output as JSON."
 ```
 
-### olmOCR2 (`olmocr2-vllm.py`)
-
-High-quality document OCR using [allenai/olmOCR-2-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8) optimized with GRPO reinforcement learning:
-
-- 🎯 **High accuracy** - 82.4 ± 1.1 on olmOCR-Bench (84.9% on math)
-- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format
-- 📊 **Table extraction** - Structured table recognition
-- 📑 **Multi-column layouts** - Complex document structures
-- 🗜️ **FP8 quantized** - Efficient 8B model for faster inference
-- 📜 **Degraded scans** - Works well on old/historical documents
-- 📝 **Long text extraction** - Headers, footers, and full document content
-- 🧩 **YAML metadata** - Structured front matter (language, rotation, content type)
-- 🚀 **Based on Qwen2.5-VL-7B** - Fine-tuned with reinforcement learning
-
-## 🆕 New Features
-
-### Multi-Model Comparison Support
-
-All scripts now include `inference_info` tracking for comparing multiple OCR models:
-
-```bash
-# First model
-uv run rolm-ocr.py my-dataset my-dataset --max-samples 100
-
-# Second model (appends to same dataset)
-uv run nanonets-ocr.py my-dataset my-dataset --max-samples 100
-
-# View all models used
-python -c "import json; from datasets import load_dataset; ds = load_dataset('my-dataset'); print(json.loads(ds[0]['inference_info']))"
-```
-
-### Random Sampling
-
-Get representative samples with the new `--shuffle` flag:
-
-```bash
-# Random 50 samples instead of first 50
-uv run rolm-ocr.py ordered-dataset output --max-samples 50 --shuffle
-
-# Reproducible random sampling
-uv run nanonets-ocr.py dataset output --max-samples 100 --shuffle --seed 42
-```
-
-### Automatic Dataset Cards
-
-Every OCR run now generates comprehensive dataset documentation including:
-
-- Model configuration and parameters
-- Processing statistics
-- Column descriptions
-- Reproduction instructions
-
-## 💻 Usage Examples
-
-### Run on HuggingFace Jobs (Recommended)
-
-No GPU? No problem! Run on HF infrastructure:
-
-```bash
-# PaddleOCR-VL - Smallest model (0.9B) with task modes
-hf jobs uv run --flavor l4x1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
-    your-input-dataset your-output-dataset \
-    --task-mode ocr \
-    --max-samples 100
-
-# PaddleOCR-VL - Extract tables from documents
-hf jobs uv run --flavor l4x1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
-    documents tables-dataset \
-    --task-mode table
-
-# PaddleOCR-VL - Formula recognition
-hf jobs uv run --flavor l4x1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
-    scientific-papers formulas-extracted \
-    --task-mode formula \
-    --batch-size 32
-
-# GLM-OCR - SOTA 0.9B model (94.62% OmniDocBench)
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
-    your-input-dataset your-output-dataset \
-    --batch-size 16 \
-    --max-samples 100
-
-# DeepSeek-OCR - Real-world example (National Library of Scotland handbooks)
-hf jobs uv run --flavor a100-large \
-    -s HF_TOKEN \
-    -e UV_TORCH_BACKEND=auto \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
-    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \
-    davanstrien/handbooks-deep-ocr \
-    --max-samples 100 \
-    --shuffle \
-    --resolution-mode large
-
-# DeepSeek-OCR - Fast testing with tiny mode
-hf jobs uv run --flavor l4x1 \
-    -s HF_TOKEN \
-    -e UV_TORCH_BACKEND=auto \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 10 \
-    --resolution-mode tiny
-
-# DeepSeek-OCR - Parse figures from scientific papers
-hf jobs uv run --flavor a100-large \
-    -s HF_TOKEN \
-    -e UV_TORCH_BACKEND=auto \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
-    scientific-papers figures-extracted \
-    --prompt-mode figure
-
-# Basic OCR job with Nanonets
-hf jobs uv run --flavor l4x1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    your-input-dataset your-output-dataset
-
-# DoTS.ocr - Multilingual OCR with compact 1.7B model
-hf jobs uv run --flavor a100-large \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
-    davanstrien/ufo-ColPali \
-    your-username/ufo-ocr \
-    --batch-size 256 \
-    --max-samples 1000 \
-    --shuffle
-
-# Real example with UFO dataset 🛸
-hf jobs uv run \
-    --flavor a10g-large \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    davanstrien/ufo-ColPali \
-    your-username/ufo-ocr \
-    --image-column image \
-    --max-model-len 16384 \
-    --batch-size 128
-
-# Nanonets OCR2 - Next-gen quality with 3B model
-hf jobs uv run \
-    --flavor l4x1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \
-    your-input-dataset \
-    your-output-dataset \
-    --batch-size 16
-
-# NuMarkdown with reasoning traces for complex documents
-hf jobs uv run \
-    --flavor l4x4 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/numarkdown-ocr.py \
-    your-input-dataset your-output-dataset \
-    --max-samples 50 \
-    --include-thinking \
-    --shuffle
-
-# olmOCR2 - High-quality OCR with YAML metadata
-hf jobs uv run \
-    --flavor a100-large \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
-    your-input-dataset your-output-dataset \
-    --batch-size 16 \
-    --max-samples 100
-
-# Private dataset with custom settings
-hf jobs uv run --flavor l40sx1 \
-    --secrets HF_TOKEN \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    private-input private-output \
-    --private \
-    --batch-size 32
-```
-
-### Python API
+**Python API:**
 
 ```python
 from huggingface_hub import run_uv_job
@@ -768,36 +201,17 @@ from huggingface_hub import run_uv_job
 job = run_uv_job(
     "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py",
     args=["input-dataset", "output-dataset", "--batch-size", "16"],
-    flavor="l4x1"
+    flavor="l4x1",
 )
 ```
 
-### Run Locally (Requires GPU)
+**Run locally** (needs your own GPU) — same scripts, run directly from the URL:
 
 ```bash
-# Clone and run
-git clone https://huggingface.co/datasets/uv-scripts/ocr
-cd ocr
-uv run nanonets-ocr.py input-dataset output-dataset
-
-# Or run directly from URL
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     input-dataset output-dataset
-
-# PaddleOCR-VL for task-specific OCR (smallest model!)
-uv run paddleocr-vl.py documents extracted --task-mode ocr
-uv run paddleocr-vl.py papers tables --task-mode table  # Extract tables
-uv run paddleocr-vl.py textbooks formulas --task-mode formula  # LaTeX formulas
-
-# RolmOCR for fast text extraction
-uv run rolm-ocr.py documents extracted-text
-uv run rolm-ocr.py images texts --shuffle --max-samples 100  # Random sample
-
-# Nanonets OCR2 for highest quality
-uv run nanonets-ocr2.py documents ocr-results
-
 ```
 
-</details>
+---
 
-Works with any HuggingFace dataset containing images — documents, forms, receipts, books, handwriting.
+Works with any Hugging Face dataset containing images — documents, forms, receipts, books, handwriting.