README.md

---
tags:
- ocr
- text-recognition
- paddleocr
- pp-ocrv6
- uv-script
- generated
dataset_info:
  config_name: lighton-ocr-2
  features:
  - name: image
    dtype: image
  - name: b_number
    dtype: string
  - name: page_index
    dtype: int64
  - name: source_row
    dtype: int64
  - name: markdown
    dtype: string
  - name: inference_info
    dtype: string
  splits:
  - name: train
    num_bytes: 20448771
    num_examples: 50
  download_size: 20340239
  dataset_size: 20448771
configs:
- config_name: lighton-ocr-2
  data_files:
  - split: train
    path: lighton-ocr-2/train-*
---

# OCR with PP-OCRv6 Medium

Plain-text OCR results for images from [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample), produced by
PaddlePaddle's [PP-OCRv6](https://huggingface.co/collections/PaddlePaddle/pp-ocrv6)
medium pipeline (34.5M (22M det + 19M rec)).

## Processing details

- **Source**: [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample)
- **Model**: PP-OCRv6_medium (PP-OCRv6_medium_det + PP-OCRv6_medium_rec)
- **Tier**: medium (34.5M (22M det + 19M rec))
- **Recognition accuracy**: 83.2%
- **Languages**: 50 languages (zh, zh-Hant, en, ja + 46 Latin-script)
- **Engine**: paddle_static
- **Samples**: 50
- **Processing time**: 1.49 min
- **Processing date**: 2026-07-08 16:42 UTC
- **License**: Apache 2.0 (models)

## Schema

Each row contains the original columns plus:

- `markdown`: Plain text extracted from the image (reading-order concatenation of
  detected text lines, newline-separated).
- `pp_ocr_blocks`: JSON list, one dict per detected text line:
  ```json
  [
    {
      "text": "recognized text",
      "score": 0.987,
      "bbox": [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
    }
  ]
  ```
  `score` is the recognition confidence and `bbox` is the detection polygon
  (4-point quadrilateral in input-image pixel coordinates).
- `inference_info`: JSON list tracking every model applied to this dataset.

> **Note:** PP-OCRv6 is a classical detection+recognition pipeline, not a VLM.
> It outputs **plain text** rather than markdown. Per-line bounding boxes and
> confidence scores are available in `pp_ocr_blocks`.

## Usage

```python
import json
from datasets import load_dataset

ds = load_dataset("davanstrien/ocr-bench-moh", split="train")
print(ds[0]["markdown"])
for block in json.loads(ds[0]["pp_ocr_blocks"]):
    print(block["text"], block["score"])
```

## Reproduction

```bash
hf jobs uv run --flavor t4-small -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-ocrv6.py \
    davanstrien/moh-bench-sample <output> --model-tier medium
```

Generated with [UV Scripts](https://huggingface.co/uv-scripts).