Parsing page structure when OCR only gives you words

What it does LayoutParser is a Python toolkit for Document Image Analysis. It wraps deep learning models behind a unified API to detect page regions such as text blocks, figures, and tables. Beyond detection, it provides layout-specific data structures that let you filter elements by spatial intervals, crop regions for OCR, and visualize results.

The interesting bit The toolkit treats page layout as a first-class data type. You can filter detected regions by spatial intervals—say, everything in the left half of a page—or pipe each cropped region straight into OCR via TesseractAgent. It also doubles as an open platform for sharing layout detection models and document-analysis pipelines, which is rarer than it should be in computer-vision tooling.

Key highlights

• Supports multiple deep learning backends for layout detection (e.g., EfficientDet on PubLayNet)
• Document-native data structures with spatial filtering, cropping, and visualization APIs
• Modular installation with optional extras for layout models and OCR
• Built-in loaders for JSON, CSV, and PDF layout data
• Open platform for sharing community models and DIA pipelines

Caveats

• Detectron2-based models require additional installation steps not covered by the standard extras.
• The README advertises a “rich repository” of models but does not list them, so you will need to consult the documentation or open platform to see what is actually available.

Verdict Worth a look if you are building document digitization pipelines or academic paper parsers and want layout-aware primitives without starting from raw PyTorch. Skip it if you need a single-backend, zero-config drop-in solution—some assembly is still required.