opendatalab/DocLayout-YOLO
A real-time document layout detection model based on YOLO-v10 trained on a 300K synthetic document dataset.
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DocLayout-YOLO is a document layout detection system that identifies and localizes document elements like text blocks, images, tables, and figures in diverse document types. It introduces Mesh-candidate BestFit, a two-dimensional bin-packing approach for synthesizing large-scale labeled document data, and a Global-to-Local Controllability module for multi-scale detection. The model is pretrained on DocSynth-300K, a 300,000-sample diverse document dataset, and achieves real-time inference speeds while maintaining accuracy across varying document layouts.
Frequently asked
- What is opendatalab/DocLayout-YOLO?
- A real-time document layout detection model based on YOLO-v10 trained on a 300K synthetic document dataset.
- Is DocLayout-YOLO open source?
- Yes — opendatalab/DocLayout-YOLO is open source, released under the AGPL-3.0 license.
- What language is DocLayout-YOLO written in?
- opendatalab/DocLayout-YOLO is primarily written in Python.
- How popular is DocLayout-YOLO?
- opendatalab/DocLayout-YOLO has 2.2k stars on GitHub.
- Where can I find DocLayout-YOLO?
- opendatalab/DocLayout-YOLO is on GitHub at https://github.com/opendatalab/DocLayout-YOLO.