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PP-DocLayoutV3_safetensors

PP-DocLayoutV3 is PaddleOCR's third-generation document layout detection model, converted to safetensors format for HuggingFace compatibility. It performs object detection to identify layout regions — text blocks, tables, figures, formulas, headings — in document images using a transformer-based backbone. The model is a building block in PaddleOCR's full document parsing pipeline.

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Fields below are copied from the tags and counters on the HuggingFace repository PaddlePaddle/PP-DocLayoutV3_safetensors at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
PaddlePaddle
Pipeline tag
object-detection
Library
Transformers
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Lineage
Language tags
multilingual; English (en), Chinese (zh)
Papers cited
arXiv:2606.23344
Downloads (HF counter at last fetch)
847,545
Likes (HF counter at last fetch)
39
Model card
https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors

Use cases

  • Identifying layout regions in scanned academic or business documents
  • Pre-processing step for downstream OCR and table extraction
  • Segmenting document pages before feeding regions to specialist models
  • Multilingual document layout analysis (English and Chinese primary)
  • Building document digitalisation pipelines with structured region extraction

Pros

  • Part of the mature PaddleOCR ecosystem with extensive documentation
  • Safetensors format makes it portable to non-PaddlePaddle frameworks
  • Strong Chinese-English bilingual layout detection out of the box
  • Transformer backbone outperforms older YOLO-based layout detectors on complex layouts

Cons

  • Optimised for Chinese-English documents; other scripts have limited evaluation
  • Full accuracy requires integration with the PaddleOCR post-processing pipeline
  • Custom architecture (pp_doclayout_v3) adds a dependency on PaddlePaddle conversion utilities
  • V3 is outperformed by multi-modal document models on pages with heavy mixed content

Tags

transformerssafetensorspp_doclayout_v3object-detectionPaddleOCRPaddlePaddleimage-segmentationocrlayoutlayout_detectionenzhmultilingualarxiv:2606.23344base_model:PaddlePaddle/PP-DocLayoutV3base_model:finetune:PaddlePaddle/PP-DocLayoutV3license:apache-2.0endpoints_compatibleregion:us