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[DOC-01]STATUS: MIT · V1.0.0

DocLayout-YOLO

Fine-tuned YOLOv8 that detects a document page's semantic layout and extracts its text.

STACK
  • YOLOv8 (fine-tuned)
  • ONNX Runtime
  • PaddleOCR
  • OpenCV
  • Streamlit
Live Demo View Source

Problem

Raw scanned and digital pages are unstructured. Downstream document AI needs regions, titles, tables, figures, text, cleanly separated before anything else can work.

Architecture

A fine-tuned YOLOv8 model exported to ONNX for cross-platform CPU/CUDA inference detects 11 region classes. PaddleOCR then extracts text per region, and the model outputs structured JSON with bounding boxes, confidence, and text: the clean substrate the rest of a document pipeline can build on.

  1. 01Page imagescanned or digital
  2. 02YOLOv8 → ONNX11 region classes
  3. 03PaddleOCRtext per region
  4. 04Structured JSONboxes · conf · text

Evidence

Region classes
11
detection
Per-region confidence
~80–96%
sample outputs
Runtime
ONNX
CPU / CUDA

The 11 classes: Title, Section-header, Text, Table, List-item, Caption, Page-header, Page-footer, Footnote, Picture, Formula.

DocLayout-YOLO structures a document; Lexora reasons over the structured result and cites its sources. Together they're one document-AI story, raw page to grounded answer.