Product Code Detection Model
Budget: ₹1,500 – ₹12,500 INR
I have a collection of scanned images from physical product stickers and need a machine-learning solution that can reliably spot the product codes printed on them. The scans vary slightly in lighting and angle, but resolution is consistent.
Here’s what I need:
• A trained model (deep-learning OCR or a custom computer-vision pipeline with TensorFlow, PyTorch, OpenCV, or equivalent) that extracts product codes from each image and outputs them in a structured format such as CSV or JSON.
• An inference script I can run locally to process new scans in batches.
• Brief documentation that explains setup, dependencies, and how to retrain if I add more sticker samples.
Acceptance will be based on:
• 95 %+ recall on a held-out test set I provide.
• Correct mapping between each filename and its detected code(s).
• Clear, reproducible instructions.
I’ll share an initial dataset of labeled scans once we start. Let me know which framework you prefer and any data requirements so I can prepare the files accordingly.
Here’s what I need:
• A trained model (deep-learning OCR or a custom computer-vision pipeline with TensorFlow, PyTorch, OpenCV, or equivalent) that extracts product codes from each image and outputs them in a structured format such as CSV or JSON.
• An inference script I can run locally to process new scans in batches.
• Brief documentation that explains setup, dependencies, and how to retrain if I add more sticker samples.
Acceptance will be based on:
• 95 %+ recall on a held-out test set I provide.
• Correct mapping between each filename and its detected code(s).
• Clear, reproducible instructions.
I’ll share an initial dataset of labeled scans once we start. Let me know which framework you prefer and any data requirements so I can prepare the files accordingly.
Related categories:
Python
Data Processing
Software Architecture
Machine Learning (ML)
OpenCV
Computer Vision
Deep Learning