# studio-dots-ai/dots.ocr

Multilingual Document Layout Parsing in a Single Vision-Language Model

Repository: https://github.com/studio-dots-ai/dots.ocr
Canonical: https://ross.abutalabs.com/products/dotsocr
Language: Python
License: MIT
License Family: permissive
Last push: 2026-03-24T14:43:26+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 73, release rhythm 35, longevity 28
- inputs: {"age_days": 399, "days_push": 162, "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 9090, forks 804 (observed 2026-08-28T04:10:28.295077+00:00)

## What it is
dots.ocr is a 1.7B-parameter vision-language model for multilingual document layout parsing, converting documents into structured output with state-of-the-art OCR performance. It also handles chart-to-SVG conversion, web screen parsing, and scene text spotting.

## Use cases
- parse documents from pdfs into structured text
- extract text from scanned documents in multiple languages
- convert charts and diagrams to svg code
- detect and read scene text in images
- parse document layout with tables and formulas
- ocr for multilingual scripts

## When to choose
- you need a single compact model for multilingual document parsing
- you want layout-aware extraction including tables, formulas, and reading order
- you need chart-to-SVG conversion or scene text spotting alongside OCR

## When to avoid
- you need a lightweight CPU-only OCR solution
- you only need simple text extraction without layout understanding
- you cannot run GPU inference for vision-language models

## Facets
- artifact type: library
- maturity: active
- function: ocr, machine-learning, pdf, image-processing, llm-inference
- domain: computer-vision, pdf, deep-learning, large-language-models
- platform: python, cross-platform
- tags: vision-language-model, document-parsing, document-layout-analysis, multilingual-ocr, svg-generation, scene-text-detection, natural-language-processing, gpu, linux

## Member repositories
- studio-dots-ai/dots.ocr (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:28.295077+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 2026-08-29T17:23:59.906100+00:00, confidence not recorded.
  - readme: https://github.com/studio-dots-ai/dots.ocr (fetched 2026-08-28T04:10:28.295077+00:00, sha 1bda813a5b96)
- Data as of 2026-08-30T08:39:29.467469+00:00.