Document Analysis on SoloSoft

GOT-OCR2.0: General OCR Theory Towards OCR-2.0 with Unified End-to-End Model

Fri, 01 May 2026 00:00:00 +0000

Optical Character Recognition has been a solved problem for decades – for clean scanned documents with straightforward text. But the real world of visual content is far messier and more diverse. Mathematical equations with complex notation, tables with irregular cell structures, musical scores with specialized symbols, and scene text on signs and labels all defy traditional OCR approaches that assume clean, linear text on uniform backgrounds.

GOT-OCR2.0 (General OCR Theory, version 2.0), developed by researchers at Ucas-HaoranWei, represents a paradigm shift toward what the authors call OCR-2.0. Instead of the traditional pipeline of detection, segmentation, and recognition modules strung together, GOT-OCR2.0 is a single end-to-end model with 580 million parameters that directly maps image pixels to structured text output.

LayoutParser: Unified Open-Source Toolkit for Document Image Analysis

Fri, 01 May 2026 00:00:00 +0000

If you have ever tried to extract structured information from a scanned PDF, a historical newspaper archive, or a stack of invoices, you know the pain: every document looks different, every model expects a different input format, and every OCR engine spits out text in a different coordinate system. LayoutParser was built to end that chaos.

Developed by the Layout-Parser team, this open-source deep learning toolkit provides a unified interface for document image analysis tasks including layout detection, OCR integration, and visual information extraction. With over 4,000 GitHub stars, LayoutParser has become the go-to library for researchers and practitioners who need to turn document images into structured, machine-readable data.

RapidLayout: Open-Source Document Layout Analysis for Chinese and English

Fri, 01 May 2026 00:00:00 +0000

Document layout analysis is the critical first step in any document understanding pipeline. Before OCR can extract text, before tables can be parsed, and before content can be classified, the system needs to understand where things are on the page. RapidLayout, an open-source library from the RapidAI team, tackles exactly this challenge with a focus on both Chinese and English document content.

Developed as part of the broader RapidAI ecosystem – which includes OCR engines, table recognition tools, and text detection models – RapidLayout provides a modular, backend-agnostic approach to layout analysis. Rather than locking users into a single inference framework, it supports OnnxRuntime, OpenVINO, and specialized CPU and GPU C++ runtimes, making it suitable for everything from edge devices to server deployments.