Dollar Bill Processor

D 51 completed
Ai Ml
unknown / python · small
357
Files
31,421
LOC
0
Frameworks
6
Languages

Pipeline State

completed
Run ID
#371842
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
42.67
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47727
Member of a group with 1 similar repo(s) — canonical #18123 view group →
Top concepts (1)
Project Description
Repobility — same analyzer, your code, free for public repos · /scan/

AI Prompt

Create a production-ready Python system for automatically processing scanned dollar bills. The goal is to identify fancy serial numbers and prepare the data for eBay listing. The system should utilize a Hybrid YOLOv8 + EasyOCR pipeline for high accuracy. Specifically, I need functionality to detect and process the six supported fancy number patterns (SOLID, REPEATER, RADAR, LADDER, LOW_SERIAL, BINARY, and STAR NOTES). The main script should take image paths, run the detection, and output a CSV file containing details like the serial number, fancy types found, confidence, and whether it's marked as fancy. Please structure the code to handle model training using `train_yolo.py` and running the main processing logic via a script like `process_bills_yolo.py`.
python yolo easyocr image-processing automation serial-number ebay computer-vision
Generated by gemma4:latest

Catalog Information

Automated processing of scanned dollar bills to identify fancy serial numbers and prepare bills for eBay listing.

Description

This project is a production-ready system that uses a hybrid YOLO Detection approach to process scanned dollar bills. It identifies fancy serial numbers and prepares the bills for eBay listing. The system consists of a Hybrid YOLOv8 + EasyOCR pipeline, which achieves a 100% detection rate on test datasets and processes up to 176 bills per minute on CPU.

الوصف

هذا المشروع هو نظام جاهز للتصديق الذي يستخدم نهج YOLO Detection hybrid لProcessing الملفات المسحاة من دولار. يIDENTIFY الأرقام المتقنة ويتجه للتحضير للملصقات على إيباي. يتألف النظام من خليط YOLOv8 + EasyOCR، والذي يحقق 100% rate detection في datasets الاختبار ويعالج حتى 176 ملفات في الدقيقة على CPU.

Novelty

7/10

Tags

fancy-serial-number-detection dollar-bill-processing ebay-listing-preparation yolo-detection easyocr

Technologies

numpy pandas pytorch

Claude Models

claude-sonnet-4.6

Quality Score

D
51.0/100
Structure
44
Code Quality
62
Documentation
43
Testing
40
Practices
46
Security
72
Dependencies
50

Strengths

  • Consistent naming conventions (snake_case)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 1 bare except/catch blocks swallowing errors
  • 2963 duplicate lines detected \u2014 consider DRY refactoring
  • 10 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)
  • Replace bare except/catch blocks with specific exception types

Security & Health

28.6h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Repobility (the analyzer behind this table) · https://repobility.com
Unknown
License
10.6%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
56.9%
lua
39.7%
markdown
2.5%
yaml
0.5%
json
0.4%
text
0.0%

Frameworks

None detected

Concepts (1)

Repobility · code-quality scanner for AI-generated software · https://repobility.com
CategoryNameDescriptionConfidence
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot
auto_descriptionProject DescriptionA production-ready system for automated processing of scanned dollar bills to identify fancy serial numbers and prepare bills for eBay listing.80%

Quality Timeline

1 quality score recorded.

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