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# document-processing

5 tools tagged

Showing 5 of 5 tools

OpenDataLoader PDF logo

OpenDataLoader PDF

AI-ready PDF parser with benchmark-leading accuracy

OpenDataLoader PDF is a high-performance parser that extracts structured, AI-ready data from PDFs with industry-leading 0.907 benchmark accuracy. Combines deterministic local processing with optional AI hybrid mode for complex layouts, OCR support across 80+ languages, formula extraction in LaTeX, chart descriptions, and built-in prompt injection filtering. Available as Python, Node.js, and Java SDKs for seamless RAG pipeline and data preparation integration.

freemiumOpen Source
WeKnora logo

WeKnora

Enterprise RAG framework by Tencent

WeKnora is a Tencent-developed LLM-powered knowledge management and Q&A framework for enterprise document understanding and semantic retrieval. Supports 10+ document formats including PDF, Word, Excel, and images with seamless IM platform integration for WeCom, Feishu, Slack, and Telegram. Offers Quick Q&A mode using RAG pipelines and Intelligent Reasoning mode with ReACT agents for complex multi-step reasoning tasks across organizational knowledge bases.

open-sourceOpen Source

Dolphin

ByteDance multimodal document image parser

Dolphin is ByteDance's multimodal document parsing model that handles intertwined text, tables, formulas, and figures in complex documents. Using a two-stage analyze-then-parse approach with a Swin Transformer vision encoder and MBart decoder, it performs layout analysis and parallel element parsing with heterogeneous anchor prompts. Dolphin-v2 adds document-type awareness for invoices, papers, and forms.

open-sourceOpen Source
RAG-Anything logo

RAG-Anything

All-in-one multimodal RAG framework

RAG-Anything is an all-in-one multimodal RAG framework from the University of Hong Kong that processes text, images, tables, and equations through a unified pipeline built on LightRAG. It constructs multi-modal knowledge graphs by extracting multimodal entities and establishing cross-modal relationships. The VLM-Enhanced Query mode integrates visual content into large language models for deeper document understanding beyond plain text retrieval.

open-sourceOpen Source

Kreuzberg

Polyglot document intelligence framework with Rust core

Kreuzberg is a polyglot document intelligence framework with a high-performance Rust core that extracts text, metadata, images, and structured data from 91+ file formats. Available for Python, Ruby, Java, Go, PHP, C#, TypeScript, plus CLI, REST API, and MCP server. Features multiple OCR backends (Tesseract, EasyOCR, PaddleOCR), table extraction with structure preservation, and native async support.

open-sourceOpen Source