# ctripcorp/C-OCR

C-OCR是携程自研的OCR项目，主要包括身份证、护照、火车票、签证等旅游相关证件、材料的识别。  项目包含4个部分，拒识、检测、识别、后处理。

Repository: https://github.com/ctripcorp/C-OCR
Canonical: https://ross.abutalabs.com/products/c-ocr
Language: Java
License Family: other
Last push: 2024-02-14T13:11:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2701, "days_push": 931, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2476, forks 625 (observed 2026-08-28T04:06:55.026882+00:00)

## What it is
C-OCR is Ctrip's in-house OCR project focused on recognizing travel-related documents such as ID cards, passports, train tickets, and visas. It is composed of four parts: rejection handling, detection, recognition, and post-processing.

## Use cases
- extract text from passport photos
- recognize chinese id cards in images
- parse train tickets with ocr
- read visa documents automatically
- detect and recognize travel documents
- ocr pipeline for identity verification

## When to choose
- you need OCR tuned for travel documents like passports, ID cards, and train tickets
- you want a Java-based OCR pipeline with detection, recognition, and post-processing stages
- you are building identity or document verification for travel or hospitality systems

## When to avoid
- you need general-purpose OCR for arbitrary documents or handwriting
- you require an actively maintained project with a clear license
- you need a non-JVM stack or pretrained models with strong community support

## Facets
- artifact type: library
- maturity: maintenance
- function: ocr, image-processing, computer-vision, nlp
- domain: computer-vision, image-processing, travel
- platform: jvm, cross-platform
- tags: travel-documents, id-card-recognition, passport, document-detection, chinese-ocr, natural-language-processing

## Member repositories
- ctripcorp/C-OCR (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:55.026882+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 2026-08-30T02:28:21.830241+00:00, confidence not recorded.
  - readme: https://github.com/ctripcorp/C-OCR (fetched 2026-08-28T04:06:55.026882+00:00, sha d6ce5391220a)
- Data as of 2026-08-30T08:39:29.467469+00:00.