# Yuliang-Liu/MonkeyOCR

A lightweight LMM-based Document Parsing Model

Repository: https://github.com/Yuliang-Liu/MonkeyOCR
Canonical: https://ross.abutalabs.com/products/monkeyocr
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-07-20T13:41:34+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 35, longevity 32
- inputs: {"age_days": 456, "days_push": 44, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6635, forks 461 (observed 2026-08-28T04:09:45.853711+00:00)

## What it is
MonkeyOCR is a lightweight large multimodal model (LMM) for document parsing that uses a Structure-Recognition-Relation triplet paradigm to convert documents (including PDFs and images) into structured text like Markdown. It provides pretrained model weights, a Python inference pipeline, and a demo, with an active v2 release line.

## Use cases
- parse pdf documents into markdown
- extract text and tables from scanned documents
- ocr for academic papers with formulas
- convert document images to structured text
- multilingual document parsing
- batch document digitization pipeline

## When to choose
- you need open-source document parsing with layout, text, formula, and table recognition
- you want a lightweight multimodal OCR model with pretrained weights
- you need multilingual document extraction in Python

## When to avoid
- you need only plain OCR without layout or structure understanding
- you have no GPU and need fast lightweight processing
- you need a fully managed cloud OCR API

## Facets
- artifact type: library
- maturity: active
- function: ocr, pdf, machine-learning, llm-inference, image-processing
- domain: artificial-intelligence, computer-vision, pdf, deep-learning
- platform: python, cross-platform
- tags: document-parsing, multimodal, vision-language-model, layout-analysis, pdf-to-markdown, natural-language-processing, gpu, linux

## Member repositories
- Yuliang-Liu/MonkeyOCR (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.853711+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 2026-08-29T17:43:12.751789+00:00, confidence not recorded.
  - readme: https://github.com/Yuliang-Liu/MonkeyOCR (fetched 2026-08-28T04:09:45.853711+00:00, sha 8c747050f096)
- Data as of 2026-08-30T08:39:29.467469+00:00.