🤖 AI Analysis

Final verdict: SUSPICIOUS

The package abstract-ocr has some concerning aspects, notably its network calls and the lack of metadata like a GitHub link, which raise suspicion about its intentions.

network calls to external URLs
lack of GitHub link for maintainer

Per-check LLM notes

Network: The network call suggests the package fetches resources from an external URL, which is common but could indicate data exfiltration if not properly documented.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and no GitHub link, which raises some suspicion but not enough to conclusively determine malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

ut_path: str): response = requests.get(url, stream=True) if response.status_code == 200:

✓ 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: abstractendeavors.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "putkoff" 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 abstract-ocr

Create a desktop application named 'DocumentReader' using Python and the 'abstract-ocr' package that allows users to extract structured text data from complex documents like invoices, receipts, and contracts. The application should have the following features:

1. **File Upload Interface**: Allow users to upload various types of scanned documents (PDFs, images).
2. **Preprocessing**: Implement a preprocessing step to clean up the document image, enhancing its quality for better OCR accuracy.
3. **Column Detection**: Utilize the 'abstract-ocr' package to detect columns in the document, which helps in organizing extracted text into meaningful sections.
4. **Region Classification**: Classify different regions within the document (e.g., header, footer, body text), making it easier to identify specific parts of interest.
5. **Text Extraction**: Use 'PaddleOCR', integrated within 'abstract-ocr', to extract text from the classified regions.
6. **Structured Output**: Present the extracted text in a structured format, such as a table or JSON object, reflecting the original layout of the document.
7. **Save/Export Options**: Provide options for users to save the extracted data in formats like CSV, JSON, or Excel.
8. **User Interface**: Develop a simple yet intuitive GUI using a toolkit like PyQt or Tkinter to interact with the application.
9. **Error Handling**: Ensure robust error handling for cases where the OCR process might fail due to low-quality scans or other issues.
10. **Performance Optimization**: Optimize the application to handle large documents efficiently, considering both time and memory usage.

The 'abstract-ocr' package will be central to this project, particularly in handling the complexities of document layouts and ensuring accurate text extraction. Your task is to design and implement this application, ensuring it meets the specified requirements while providing a user-friendly experience.