Doc-Qual

v0.1.0 suspicious
6.0
Medium Risk

Image quality scoring for OCR readiness

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell execution, obfuscation, and credential harvesting. However, metadata risk is elevated due to suspicious git repository activity and maintainer history, raising concerns about potential malicious intent.

  • Elevated metadata risk
  • Suspicious git repository activity
  • Unclear maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package relies on external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being potentially malicious due to suspicious git repository activity and maintainer history.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Single contributor with only 3 commit(s) — possibly throwaway account

  • Single contributor with only 3 commit(s) — possibly throwaway account
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Doc-Qual 
Develop a Python-based mini-application named 'OCR-Quality-Assistant' that leverages the 'Doc-Qual' package to assess the quality of images before they undergo Optical Character Recognition (OCR) processing. This tool will help users determine if their document images are suitable for OCR by providing a quality score and suggesting improvements if necessary. Here’s a step-by-step guide on what your application should include:

1. **Image Upload**: Allow users to upload image files from their local machine or provide a feature to directly input URLs of images.
2. **Quality Scoring**: Utilize the 'Doc-Qual' package to analyze each uploaded image and generate a quality score indicating its suitability for OCR.
3. **Feedback Mechanism**: Based on the quality score, provide feedback to the user. If the score is below a certain threshold (e.g., 70 out of 100), suggest methods to improve the image quality such as adjusting lighting, reducing noise, or improving contrast.
4. **Enhancement Tools**: Integrate simple image enhancement tools within the app that users can use to improve the image quality according to the suggestions provided.
5. **Report Generation**: Offer the ability to generate a report summarizing the quality assessment and any recommended enhancements.
6. **User Interface**: Design a user-friendly interface that makes it easy for users to navigate through the process, view results, and access enhancement tools.

This application will be invaluable for anyone needing to prepare documents for OCR processing, ensuring higher accuracy in text recognition and saving time and resources.