awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
rednote-hilab avatar

rednote-hilab/dots.ocr

0
View on GitHub↗

Dots.ocr

dots.ocr is a suite of software utilities for document layout analysis, multilingual optical character recognition, and scene text digitization. It functions as an engine for extracting digital text and structured layout data from images and PDFs across various human scripts.

The project includes a specialized transformer for converting charts, diagrams, and chemical formulas from raster images into scalable vector graphics. It also provides a pipeline to transform extracted text and structural layout from documents and web screenshots into formatted Markdown files.

The system covers capabilities for identifying bounding boxes and categories of layout elements to produce structured JSON representations. It further includes tools for scene text detection within natural images and an evaluation framework for measuring text and table extraction accuracy against ground truth data.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Features

  • Document Analysis Tools - Provides a comprehensive tool for detecting bounding boxes and layout categories to structure documents as JSON.
  • Bounding Box Detection - Provides bounding box detection to locate page elements and distinguish body text from headers and footers.
  • Multilingual OCR Systems - Extracts digital text and structured layout data from images and PDFs across various human scripts.
  • Document Layout Analysis - Identifies bounding boxes and categories for layout elements within images and PDF files.
  • Multilingual Text Recognition - Implements a unified recognition engine to process diverse character sets and writing systems from images into digital text.
  • Scene Text Recognition - Locates and extracts digital text from natural images and complex environmental backgrounds.
  • OCR-Based Extraction - Extracts digital text and structured layout data from images and PDFs across various human scripts.
  • Scene Text Detection Systems - Finds and digitizes text embedded within natural scene images and web screenshots.
  • Document Digitization Tools - Converts images and PDFs containing various human scripts into digital text while preserving document structure.
  • Automated Generation - Transforms extracted text and structural layout from documents and web screenshots into formatted Markdown files.
  • Document Layout Analyzers - Identifies bounding boxes and categories of layout elements to produce structured JSON representations of documents.
  • Markdown Converters - Transforms extracted text and structural layout from images and documents into formatted Markdown files.
  • Visual-to-Markdown Pipelines - Transforms scanned documents and web screenshots into formatted Markdown files for editing and archival.
  • Layout-Aware Extraction - Maps spatial relationships between text elements and document structures during the extraction process.
  • Layout-Preserving Export - Generates formatted Markdown files from extracted document text while excluding page headers and footers.
  • Raster-to-Vector Conversions - Transforms rasterized charts, diagrams, and chemical formulas into scalable vector graphics.
  • Model Serving Engines - Implements high-performance model serving engines to provide scalable text extraction API endpoints.
  • Model Serving Servers - Serves machine learning models as scalable API endpoints using high-performance inference servers.
  • Text Detection Models - Locates and digitizes text embedded within natural scene images and complex backgrounds.
  • OCR Accuracy Evaluators - Measures text and table extraction accuracy against ground truth images to refine OCR performance.
  • Visual Layout Parsing - Extracts structured text and layout information specifically from screenshots of web pages.
  • Vector Graphics Export - Converts technical graphics like charts and chemical formulas from images into scalable vector graphics.
  • Raster-to-SVG Transformers - Converts charts, diagrams, and chemical formulas from raster images into scalable vector graphics code.
7,695 stars·707 forks·Python·mit·49 views

Star history

Star history chart for rednote-hilab/dots.ocrStar history chart for rednote-hilab/dots.ocr

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Dots.ocr

These projects share indexed features with Dots.ocr. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • breezedeus/pix2textbreezedeus avatar

    breezedeus/Pix2Text

    3,012View on GitHub↗

    Pix2Text is an optical character recognition system and document conversion tool designed to transform images and PDFs into Markdown. It functions as a multilingual OCR engine supporting over 80 languages, a LaTeX formula recognizer for mathematical notations, and a parser integrated with vision language models. The project utilizes a hybrid pipeline to separate plain text from mathematical formulas and tabular structures within a single pass. It converts recognized formulas into LaTeX expressions and transforms detected tables and layouts into structured Markdown formatting. The system incl

    Jupyter Notebookimage-to-markdownlatexlatex-pdf
    View on GitHub↗3,012
  • axa-group/parsraxa-group avatar

    axa-group/Parsr

    6,178View on GitHub↗

    Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned, machine-readable formats. It functions as a document layout analyzer and a pipeline for extracting structured data and labels using large language models. The system includes a document parsing visualizer, providing a graphical interface to upload documents and inspect the resulting structured data output. The project covers document digitization workflows, including layout analysis to detect headings, tables, and lists, and automated data entry through the cleaning and enri

    JavaScript
    View on GitHub↗6,178
  • microsoft/unilmmicrosoft avatar

    microsoft/unilm

    22,030View on GitHub↗

    This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations. The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec

    Pythonbeitbeit-3bitnet
    View on GitHub↗22,030
  • bytedance/dolphinbytedance avatar

    bytedance/Dolphin

    8,820View on GitHub↗

    Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital document images into machine-readable structured data. It functions as an LLM document parser, utilizing vision-language models to simultaneously predict spatial layout and text content. The system is designed as a concurrent document processor, employing parallel document parsing to process multiple elements across distributed compute nodes. This high-throughput approach reduces the total time required to convert large volumes of images into structured formats. The project covers

    Pythondocument-analysislayout-analysisocr
    View on GitHub↗8,820
Compare all 30 related projects→

Frequently asked questions

What does rednote-hilab/dots.ocr do?

dots.ocr is a suite of software utilities for document layout analysis, multilingual optical character recognition, and scene text digitization. It functions as an engine for extracting digital text and structured layout data from images and PDFs across various human scripts.

What are the main features of rednote-hilab/dots.ocr?

The main features of rednote-hilab/dots.ocr are: Document Analysis Tools, Bounding Box Detection, Multilingual OCR Systems, Document Layout Analysis, Multilingual Text Recognition, Scene Text Recognition, OCR-Based Extraction, Scene Text Detection Systems.

Which projects share features with rednote-hilab/dots.ocr?

Projects with overlapping indexed features include: axa-group/parsr — Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned,… breezedeus/pix2text — Pix2Text is an optical character recognition system and document conversion tool designed to transform images and PDFs… microsoft/unilm — This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based… bytedance/dolphin — Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital… vikparuchuri/marker — Marker is an LLM-powered document parser and OCR pipeline designed to convert PDFs and unstructured files into… getomni-ai/zerox — Zerox is a multimodal document parser and OCR tool that uses vision models to convert PDF files and images into…