Southview Ocr

C+ 76 completed
Data Tool
unknown / python · small
68
Files
2,636
LOC
3
Frameworks
5
Languages

Pipeline State

completed
Run ID
#347313
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
59.59
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #55842
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.

AI Prompt

I want to build a tool for historical index card digitization, similar to the southview-ocr project. Can you set up the basic structure using Python, FastAPI, and SQLAlchemy? The project should handle configuration via a YAML file, include a testing suite using pytest, and have separate directories for the frontend, data, and documentation. Please ensure the project structure is ready for OCR processing.
python fastapi sqlalchemy ocr digitization backend testing config
Generated by gemma4:latest

Catalog Information

The southview-ocr project is a tool for historical index card digitization.

Description

This project is an automated pipeline for digitizing historical index cards. It likely uses machine learning and computer vision techniques to extract text from images of the cards. The goal is to make it easier to search, organize, and preserve historical data. This project could be useful for archivists, researchers, or anyone working with large collections of physical documents.

الوصف

هذا المشروع هو مصفوفة تلقائية لتحويل بطاقات مؤشرات تاريخية إلى رقمية. يعتمد على تقنيات التعلم الآلي والتصوير الحاسوبي للتعرف على النصوص في صور البطاقات. الهدف من هذا المشروع هو جعل عملية البحث والتسجيل والاحتفاظ بالبيانات التاريخية أسهل. يمكن أن يكون مفيدًا للمحافظين على الأرشيف، والمبحثين، أو أي شخص يعمل مع مجموعات كبيرة من الوثائق المادية.

Novelty

7/10

Tags

historical-document-digitization index-card-scanning text-extraction computer-vision machine-learning

Technologies

fastapi numpy pydantic sqlalchemy uvicorn

Claude Models

claude-opus-4.6

Quality Score

C+
76.0/100
Structure
70
Code Quality
99
Documentation
50
Testing
60
Practices
71
Security
100
Dependencies
60

Strengths

  • Good test coverage (47% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected

Weaknesses

  • Missing README file \u2014 critical for project understanding
  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Add a comprehensive README.md explaining purpose, setup, usage, and architecture
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.6h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (4)
About: code-quality intelligence by Repobility · https://repobility.com
Unknown
License
1.8%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
62.8%
markdown
34.3%
yaml
1.3%
toml
1.2%
text
0.4%

Frameworks

FastAPI pytest SQLAlchemy

Concepts (2)

Scored by Repobility's multi-pass pipeline · https://repobility.com
CategoryNameDescriptionConfidence
Want fix-PRs on findings? Install Repobility's GitHub App · github.com/apps/repobility-bot
auto_descriptionProject DescriptionHistorical index card digitization pipeline80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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