#python #computer_vision #deep_learning #detectron2 #document_image_analysis #document_image_processing #document_layout_analysis #layout_analysis #layout_parser #object_detection #ocr
https://github.com/Layout-Parser/layout-parser
https://github.com/Layout-Parser/layout-parser
GitHub
GitHub - Layout-Parser/layout-parser: A Unified Toolkit for Deep Learning Based Document Image Analysis
A Unified Toolkit for Deep Learning Based Document Image Analysis - Layout-Parser/layout-parser
#swift #auto #autolayout #cocoapods #constraints #dsl #layout #snapkit #ui #xcode
https://github.com/SnapKit/SnapKit
https://github.com/SnapKit/SnapKit
GitHub
GitHub - SnapKit/SnapKit: A Swift Autolayout DSL for iOS & OS X
A Swift Autolayout DSL for iOS & OS X. Contribute to SnapKit/SnapKit development by creating an account on GitHub.
#python #ai4science #document_analysis #extract_data #layout_analysis #ocr #parser #pdf #pdf_converter #pdf_extractor_llm #pdf_extractor_pretrain #pdf_extractor_rag #pdf_parser #python
MinerU is a tool that converts PDFs into machine-readable formats like markdown or JSON. Here are the key benefits and features MinerU removes headers, footers, and other unnecessary elements to ensure the text is semantically coherent and in human-readable order, even for complex layouts.
- **Structure Preservation** It extracts images, image descriptions, tables, and table titles.
- **Formula Conversion** Recognizes tables and converts them to LaTeX or HTML format.
- **OCR Support** Supports multiple output formats and various visualization results.
- **GPU and CPU Compatibility**: Works on both CPU and GPU environments, compatible with Windows, Linux, and Mac.
You can try MinerU through an online demo, a quick CPU demo, or by using a GPU for faster processing. For detailed usage, refer to the command line options, API integration, and deployment guides provided.
https://github.com/opendatalab/MinerU
MinerU is a tool that converts PDFs into machine-readable formats like markdown or JSON. Here are the key benefits and features MinerU removes headers, footers, and other unnecessary elements to ensure the text is semantically coherent and in human-readable order, even for complex layouts.
- **Structure Preservation** It extracts images, image descriptions, tables, and table titles.
- **Formula Conversion** Recognizes tables and converts them to LaTeX or HTML format.
- **OCR Support** Supports multiple output formats and various visualization results.
- **GPU and CPU Compatibility**: Works on both CPU and GPU environments, compatible with Windows, Linux, and Mac.
You can try MinerU through an online demo, a quick CPU demo, or by using a GPU for faster processing. For detailed usage, refer to the command line options, API integration, and deployment guides provided.
https://github.com/opendatalab/MinerU
GitHub
GitHub - opendatalab/MinerU: Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.
Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows. - opendatalab/MinerU
#c_lang #layout #ui
Clay is a powerful 2D UI layout library that offers several key benefits Clay provides microsecond layout performance, making it suitable for complex and responsive UIs.
- **Flexible Layouts** The library is contained in a single 2k LOC file (`clay.h`) with zero dependencies, including no standard library.
- **Cross-Platform** It uses static arena-based memory management with low total memory overhead.
- **Declarative Syntax** Users can extend the library with custom elements and configurations.
To get started, you need to include `clay.h`, define the necessary memory arena, set up the layout dimensions, and begin declaring your UI elements using the provided macros. This makes Clay a versatile and efficient tool for building complex UIs.
https://github.com/nicbarker/clay
Clay is a powerful 2D UI layout library that offers several key benefits Clay provides microsecond layout performance, making it suitable for complex and responsive UIs.
- **Flexible Layouts** The library is contained in a single 2k LOC file (`clay.h`) with zero dependencies, including no standard library.
- **Cross-Platform** It uses static arena-based memory management with low total memory overhead.
- **Declarative Syntax** Users can extend the library with custom elements and configurations.
To get started, you need to include `clay.h`, define the necessary memory arena, set up the layout dimensions, and begin declaring your UI elements using the provided macros. This makes Clay a versatile and efficient tool for building complex UIs.
https://github.com/nicbarker/clay
GitHub
GitHub - nicbarker/clay: High performance UI layout library in C.
High performance UI layout library in C. Contribute to nicbarker/clay development by creating an account on GitHub.
#lua #layout #lua #neovim #neovim_plugin #neovim_ui #nvim #plugin #scratchpad #ui #ux #zen_mode #zenmode
The no-neck-pain.nvim plugin for Neovim centers your active editing window by adding empty buffers on each side, creating padding that keeps your focus in the middle of the screen. It works right away without setup, supports multiple tabs, split windows, and integrates with popular file tree and dashboard plugins. You can customize its width, colors, and behavior, and even use the side buffers as scratchpads for notes. This helps reduce neck strain and improves focus, especially on wide monitors, by keeping your code or text centered and easy to read without distractions.
https://github.com/shortcuts/no-neck-pain.nvim
The no-neck-pain.nvim plugin for Neovim centers your active editing window by adding empty buffers on each side, creating padding that keeps your focus in the middle of the screen. It works right away without setup, supports multiple tabs, split windows, and integrates with popular file tree and dashboard plugins. You can customize its width, colors, and behavior, and even use the side buffers as scratchpads for notes. This helps reduce neck strain and improves focus, especially on wide monitors, by keeping your code or text centered and easy to read without distractions.
https://github.com/shortcuts/no-neck-pain.nvim
GitHub
GitHub - shortcuts/no-neck-pain.nvim: ☕ Dead simple yet super extensible zen mode plugin to protect your neck.
☕ Dead simple yet super extensible zen mode plugin to protect your neck. - shortcuts/no-neck-pain.nvim
❤1
#python #document_analysis #layout_analysis #ocr #parser #pdf #pdf_converter #pdf_parser #python #vlm_ocr
Dolphin is a smart AI tool that can analyze and understand complex document images, like pages with text, tables, formulas, and pictures. It works in two steps: first, it figures out the layout and reading order of the page; then, it quickly parses each element using special prompts. This makes it fast and accurate for turning document images into structured data like JSON or Markdown. You can use pre-trained models and easy code to process single pages, PDFs, or specific elements. This helps you save time and effort when extracting information from complicated documents efficiently.
https://github.com/bytedance/Dolphin
Dolphin is a smart AI tool that can analyze and understand complex document images, like pages with text, tables, formulas, and pictures. It works in two steps: first, it figures out the layout and reading order of the page; then, it quickly parses each element using special prompts. This makes it fast and accurate for turning document images into structured data like JSON or Markdown. You can use pre-trained models and easy code to process single pages, PDFs, or specific elements. This helps you save time and effort when extracting information from complicated documents efficiently.
https://github.com/bytedance/Dolphin
GitHub
GitHub - bytedance/Dolphin: The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025. - bytedance/Dolphin