anpr-pipeline

v0.2.3 suspicious
4.0
Medium Risk

Automatic Number Plate Recognition — 2026 modernized pipeline (fast-alpr + FastAPI)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network usage, shell execution, obfuscation, and credential handling. However, the incomplete and possibly inactive maintainer profile raises concerns about the package's long-term support and potential for future malicious updates.

  • Incomplete maintainer profile
  • Possibly inactive maintainer
Per-check LLM notes
  • Network: No network calls suggest the package does not engage in external communications, which is normal unless specific network functionality is expected.
  • Shell: No shell executions indicate that the package does not perform system-level operations, reducing risk of unauthorized access or command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The maintainer has an incomplete profile and seems to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10253 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 52 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 69 commits in mftnakrsu/Automatic_Number_Plate_Recognition_YOLO_OCR
  • Active community — 5 or more distinct contributors

🔬 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

Repository mftnakrsu/Automatic_Number_Plate_Recognition_YOLO_OCR appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 anpr-pipeline 
Your task is to create a web-based mini-application that leverages the 'anpr-pipeline' package to recognize vehicle number plates from uploaded images. This application will serve as a practical demonstration of how to integrate advanced computer vision techniques into a user-friendly web interface. Here’s a detailed breakdown of the project requirements:

1. **Project Overview**: Develop a web application using FastAPI and the 'anpr-pipeline' package. This app should allow users to upload images containing vehicle number plates and receive the recognized plate numbers as output.

2. **Application Features**:
   - **User Interface**: Design a simple yet intuitive UI where users can select and upload images.
   - **Image Processing**: Use 'anpr-pipeline' to process the uploaded image and extract the number plate information.
   - **Output Display**: Show the recognized number plate text and optionally highlight the detected area on the image.
   - **Error Handling**: Implement robust error handling to manage cases where the image does not contain a number plate or if the image is corrupted.
   - **Logging**: Include logging functionality to record successful and failed attempts.

3. **Implementation Steps**:
   - **Setup Environment**: Ensure you have Python installed along with FastAPI and 'anpr-pipeline'.
   - **Create FastAPI Application**: Set up a basic FastAPI server with endpoints for uploading images and returning results.
   - **Integrate 'anpr-pipeline'**: Use 'anpr-pipeline' within your FastAPI application to handle the image processing logic.
   - **Develop UI**: Create a frontend interface using HTML/CSS/JavaScript (or a framework like React) that communicates with your FastAPI backend via REST API calls.
   - **Testing**: Test the application thoroughly to ensure it handles various scenarios effectively, including edge cases.

4. **Deliverables**:
   - A fully functional web application that allows users to upload images and see the recognized number plate text.
   - Source code documentation detailing the setup, integration, and usage of 'anpr-pipeline'.
   - A brief report discussing any challenges faced during development and how they were resolved.

5. **Additional Considerations**:
   - Ensure the application is secure, especially regarding file uploads.
   - Optimize performance for faster response times.
   - Consider adding features like support for multiple languages or regions if 'anpr-pipeline' supports them.

💬 Discussion Feed

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