# alibaba/Logics-Parsing

Repository: https://github.com/alibaba/Logics-Parsing
Canonical: https://ross.abutalabs.com/products/logics-parsing
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-05-13T03:12:13+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 82, release rhythm 35, longevity 25
- inputs: {"age_days": 356, "days_push": 112, "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 1402, forks 121 (observed 2026-08-28T04:04:37.346478+00:00)

## What it is
Logics-Parsing is an end-to-end document parsing model from Alibaba that converts document images into structured output using a single multimodal LLM, handling complex layouts, scientific formulas, chemical structures (SMILES), tables, and handwritten text. Version 2 extends this to Parsing-2.0 scenarios such as musical sheets, flowcharts, and code/pseudocode blocks.

## Use cases
- parse scanned pdf documents into structured markdown
- extract tables and formulas from document images
- convert chemical structure diagrams to SMILES
- ocr handwritten documents
- parse flowcharts and code blocks from images
- build rag pipelines over document corpora
- benchmark document parsing models on OmniDocBench

## When to choose
- you need end-to-end document image to structured text without multi-stage pipelines
- documents contain complex layouts, formulas, or chemical structures
- you want a single-model deployment for document parsing

## When to avoid
- you need lightweight CPU-only OCR without GPU inference
- you only need plain text extraction from digital PDFs
- you need a mature commercial document service with support

## Facets
- artifact type: library
- maturity: active
- function: ocr, nlp, machine-learning, pdf, parser
- domain: computer-vision, pdf, artificial-intelligence, deep-learning
- platform: python
- tags: document-parsing, document-understanding, multimodal-llm, layout-analysis, table-recognition, formula-recognition, smiles, end-to-end, natural-language-processing, gpu, linux

## Member repositories
- alibaba/Logics-Parsing (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:37.346478+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 2026-08-30T04:38:57.942491+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/Logics-Parsing (fetched 2026-08-28T04:04:37.346478+00:00, sha c2ff6b23ec9c)
- Data as of 2026-08-30T08:39:29.467469+00:00.