davanstrien/pp-ocrv6-smoke-v4

OCR with PP-OCRv6 TINY

Plain-text OCR results for images from davanstrien/ufo-ColPali, produced by PaddlePaddle's PP-OCRv6 tiny pipeline (1.5M (0.4M det + 1.1M rec)).

Processing details

• Source: davanstrien/ufo-ColPali
• Model: PP-OCRv6_tiny (PP-OCRv6_tiny_det + PP-OCRv6_tiny_rec)
• Tier: tiny (1.5M (0.4M det + 1.1M rec))
• Recognition accuracy: 73.5%
• Languages: 49 languages (en, zh only — no ja)
• Engine: paddle_static
• Samples: 3
• Processing time: 0.41 min
• Processing date: 2026-06-23 10:19 UTC
• License: Apache 2.0 (models)

Schema

Each row contains the original columns plus:

• text: 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("{output_dataset_id}", split="train")
print(ds[0]["text"])
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/ufo-ColPali <output> --model-tier tiny

Generated with UV Scripts.