README.md

metadata

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, produced by PaddlePaddle's PP-OCRv6 medium pipeline (34.5M (22M det + 19M rec)).

Processing details

• Source: 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:

  [
    {
      "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

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

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.