Popular GitHub Repositories for Force Layout

MCP server to provide Figma layout information to AI coding agents like Cursor

All-in-One Development Tool based on PaddlePaddle（飞桨低代码开发工具）

A Unified Toolkit for Deep Learning Based Document Image Analysis

OCR model that handles complex tables, forms, handwriting with full layout.

A Repo For Document AI

An Open-Source Python3 tool with SMALL models for recognizing layouts, tables, math formulas (LaTeX), and text in images, converting them into Markdown format. A free alternative to Mathpix, empowering seamless conversion of visual content into text-based representations. 80+ languages are supported.

A QoS-based scheduling system brings optimal layout and status to workloads such as microservices, web services, big data jobs, AI jobs, etc.

A curated list of resources for Document Understanding (DU) topic

Force Remove Copilot and Recall in Windows

Advanced LLM-powered brute-force tool combining AI intelligence with automated login attacks

Yomitoku is an AI-powered document image analysis package designed specifically for the Japanese language.

OCR engine for all the languages

LAYRA is a ready-to-use visual RAG system with a complete UI built with Next.js and FastAPI, preserving document layout, tables, paragraphs, and graphical elements without any structural fragmentation.

CnSTD: 基于 PyTorch/MXNet 的 中文/英文 场景文字检测（Scene Text Detection）、数学公式检测（Mathematical Formula Detection, MFD）、篇章分析（Layout Analysis）的Python3 包

HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!

Accurate Neural Network Potential on PyTorch

MatterSim: A deep learning atomistic model across elements, temperatures and pressures.

The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field

Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov