Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,58 @@
+---
+tags:
+- ocr
+- text-recognition
+- tesseract
+- uv-script
+- generated
+---
+
+# Document OCR using Tesseract
+
+This dataset contains OCR results from images in [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample) using [Tesseract](https://github.com/tesseract-ocr/tesseract), the classical open-source CPU OCR engine — a cheap, no-GPU baseline alongside the VLM OCR recipes.
+
+## Processing Details
+
+- **Source Dataset**: [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample)
+- **Engine**: [Tesseract](https://github.com/tesseract-ocr/tesseract) `5.3.0`
+- **Language(s)**: `eng`
+- **Number of Samples**: 50
+- **Processing Time**: 0.5 min
+- **Processing Date**: 2026-07-08 16:38 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Page Segmentation Mode (psm)**: 3
+- **OCR Engine Mode (oem)**: 3
+- **Workers**: 64
+
+## Model Information
+
+Tesseract is a classical (non-VLM) OCR engine:
+- Runs on CPU — no GPU required
+- v4+ uses an LSTM-based recognition engine
+- 100+ languages via installable data packs
+- Plain-text output (no markdown / table / formula structure)
+- Apache-2.0 licensed
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The recognised text (plain text)
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Reproduction
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/tesseract-ocr.py \
+    davanstrien/moh-bench-sample \
+    <output-dataset> \
+    --image-column image \
+    --lang eng \
+    --psm 3
+```
+
+Generated with [UV Scripts](https://huggingface.co/uv-scripts)