OCR 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.

Marker: Open-Source PDF to Markdown Conversion with Deep Learning

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

PDF documents remain one of the most common formats for knowledge distribution, yet they are among the most difficult to process programmatically. Tables split across pages, multi-column layouts, mathematical equations, headers, and footers all conspire to defeat naive extraction tools. Marker tackles this challenge with a deep learning approach that understands document structure the way a human reader does – by recognizing visual layout patterns, not just following text order.

Created by the datalab-to team, Marker builds upon recent advances in computer vision and document understanding to produce high-quality Markdown output from PDF inputs. Unlike traditional PDF converters that rely on heuristic rules or positional text extraction, Marker uses neural network models trained on thousands of annotated document pages to understand layout semantics, detect tables and equations, and reconstruct the intended reading order.

MinerU: Open-Source PDF Document Parsing and Data Extraction

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

PDF is the universal format for document distribution, but it is arguably the worst format for data extraction. PDFs store visual layouts — coordinates, fonts, and rendering instructions — not semantic structure. Paragraphs, tables, lists, and headings exist only as visual arrangements of text fragments. Every developer who has tried to extract structured data from a PDF knows the frustration of losing table structure, mangled text order, and jumbled multi-column layouts.

MinerU, developed by OpenDataLab, addresses this problem with a comprehensive open-source document parsing pipeline. It extracts text, tables, formulas, and images from PDFs with high structural fidelity, producing clean Markdown or structured JSON output. For organizations building RAG systems, knowledge bases, or data processing pipelines, MinerU fills the critical gap between raw PDF files and machine-readable content.

olmOCR: AI2's Open-Source PDF-to-Markdown Toolkit for LLM Training Data

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

Converting PDFs to clean, machine-readable text at scale is one of the foundational challenges in LLM dataset preparation. Traditional PDF parsers struggle with complex layouts, tables, and mixed content, while commercial OCR services are expensive at scale. olmOCR by Allen AI (AI2) solves this problem using a 7B parameter Vision-Language Model that converts PDF pages into clean Markdown with remarkable accuracy and cost efficiency.

The key insight behind olmOCR is treating PDF conversion as a vision-language task rather than a text extraction problem. Instead of parsing the underlying PDF structure (which is often unreliable for complex layouts), olmOCR renders each page to an image and uses its VLM to read and transcribe the content, preserving layout, structure, and semantics.

PaddleOCR: Baidu's Ultra-Lightweight OCR Toolkit with 80+ Language Support

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

PaddleOCR is Baidu’s industrial-grade, ultra-lightweight optical character recognition (OCR) toolkit built on the PaddlePaddle deep learning framework. As one of the most popular open-source OCR projects on GitHub, PaddleOCR has evolved through multiple major versions – now at PP-OCRv5 for text detection and recognition, PP-StructureV3 for comprehensive document parsing, and PP-ChatOCRv4 for LLM-powered document intelligence.

What sets PaddleOCR apart is its combination of accuracy, speed, and breadth. The PP-OCRv5 model achieves state-of-the-art accuracy while maintaining a model size of under 15 MB for the full detection and recognition pipeline. Support spans over 80 languages, and the toolkit includes everything from text detection and recognition to document layout analysis, table extraction, and even LLM-based question answering over documents.

PDF-Extract-Kit: Comprehensive PDF Content Extraction Toolkit

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

PDFs remain the most common format for document exchange, but extracting structured content from them is notoriously difficult. PDF-Extract-Kit, developed by OpenDataLab, combines deep learning models with traditional rule-based methods to extract text, tables, formulas, and images with remarkable accuracy.

The toolkit addresses the full spectrum of PDF extraction challenges. Scanned documents are handled with OCR, digital PDFs use direct text extraction, complex layouts are analyzed with layout detection models, and mathematical formulas are parsed with specialized equation recognition. The output is structured Markdown or JSON that preserves the document’s logical structure.

Extraction Capabilities

Content Type	Method	Accuracy
Text (digital)	Direct extraction	99%+
Text (scanned)	OCR with layout analysis	96%+
Tables	Deep learning detection + structure recognition	92%+
Formulas	LaTeX recognition from images	88%+
Images	Region detection + extraction	95%+

Extraction Pipeline

flowchart LR
 A[PDF File] --> B{Document Type?}
 B -->|Digital PDF| C[Direct Text Extraction]
 B -->|Scanned PDF| D[OCR Pipeline]
 C --> E[Layout Analysis]
 D --> E
 E --> F{Content Type}
 F -->|Text| G[Text Segment]
 F -->|Table| H[Table Structure Recognition]
 F -->|Formula| I[LaTeX Parsing]
 F -->|Image| J[Image Extraction]
 G --> K[Markdown/JSON Output]
 H --> K
 I --> K
 J --> K

The pipeline intelligently routes documents based on whether they are digital or scanned. After text extraction, layout analysis identifies different content regions, and specialized models handle each type of content independently before merging everything into a structured output.

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.

Surya: Open-Source Multilingual OCR and Document Understanding

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

Optical Character Recognition is one of the oldest applications of computer vision, but traditional OCR engines have struggled to keep pace with modern demands. Documents today are more diverse in layout, multilingual in content, and variable in quality than ever before. Surya represents a modern approach to OCR, built on deep learning architectures that handle the complexity of real-world documents with accuracy that traditional engines cannot match.

Developed by the datalab-to team (the same group behind Marker), Surya is designed as both a standalone OCR system and a component for larger document processing pipelines. It provides three core capabilities: text detection (finding where text is on a page), text recognition (reading what it says), and layout analysis (understanding the document structure). The unified architecture means that a single model handles text across dozens of scripts and languages.