# Yuliang-Liu/MonkeyOCRv2

MonkeyOCRv2 Vision Encoder — A Document-Native Visual Backbone

Repository: https://github.com/Yuliang-Liu/MonkeyOCRv2
Canonical: https://ross.abutalabs.com/products/monkeyocrv2
Homepage: https://yuliang-liu.github.io/MonkeyOCRv2/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-09-02T08:41:32+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 35, longevity 3
- inputs: {"age_days": 54, "days_push": 0, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1256, forks 125 (observed 2026-09-03T02:15:08.650082+00:00)

## What it is
MonkeyOCRv2 is a document-native vision encoder and visual-text foundation model for Document AI, pretrained on the 113M-image MonkeyDoc v2 corpus across 17 languages. It ships as a standalone vision backbone plus downstream models for document parsing, text/formula recognition, detection, and document understanding.

## Use cases
- parse documents into markdown
- extract text from scanned pdfs
- recognize math formulas in images
- detect text in scene photos
- use a document vision encoder for downstream models
- run multilingual ocr on photographed documents
- detect document tampering

## When to choose
- you need state-of-the-art open-source document parsing across many languages
- you want a document-native vision backbone to replace CLIP/DINO/SAM encoders
- you need fast inference via vLLM or CPU-only parsing

## When to avoid
- you only need OCR on natural photos with sparse text
- you need a permissively licensed model (license is not standard)
- you want a general-purpose vision model for non-document imagery

## Facets
- artifact type: library
- maturity: active
- function: ocr, machine-learning, deep-learning, image-processing, llm-inference
- domain: artificial-intelligence, computer-vision, pdf, large-language-models
- platform: python
- tags: document-ai, vision-encoder, foundation-model, document-parsing, pretrained-model, multilingual-ocr, formula-recognition, vllm, natural-language-processing, linux, gpu, docker

## Member repositories
- Yuliang-Liu/MonkeyOCRv2 (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:08.650082+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:22:41.477160+00:00, confidence not recorded.
  - readme: https://github.com/Yuliang-Liu/MonkeyOCRv2 (fetched 2026-09-03T02:15:08.650082+00:00, sha 834909e692b4)
  - homepage: https://yuliang-liu.github.io/MonkeyOCRv2/ (fetched 2026-08-29T12:30:13.569555+00:00, sha 02d58712037f)
- Data as of 2026-08-30T08:39:29.467469+00:00.