This entry presents an end-to-end AI-based system designed to autonomously categorize retail products and extract key information for supply chain optimization, quality control, and retail analytics. The solution utilizes a multi-modal deep learning pipeline combining image classification, optical character recognition (OCR), natural language processing (NLP), and rule-based reasoning to analyze product images—particularly distinguishing between perishable (fruits and vegetables) and packaged items—and extracting actionable information like manufacturing and expiry dates, MRP, quantity, and shelf life.

System Architecture and Working

The system initiates with a custom-trained image classification model based on YOLOv11 to determine the product type. For fruits and vegetables, a CNN-based freshness detection module assesses the product’s quality based on visual features. For packaged items, a fine-tuned PaddleOCR engine (optimized for English) extracts printed text, followed by a SpaCy-based NLP pipeline that identifies and extracts entities such as manufacturing date, expiry date, quantity, and MRP. These are then processed using dateparser and regex patterns to convert them into a structured JSON format. The system also calculates shelf life by computing the delta between manufacturing and expiry dates.

Innovation:

The novelty of the design lies in its holistic integration of visual classification, OCR, and NLP into a unified product intelligence pipeline. Unlike traditional barcode scanning or isolated OCR tools, this system contextualizes visual and textual data for higher semantic understanding. Its ability to distinguish between perishables and packaged goods and apply distinct processing paths (freshness detection vs. date/MRP extraction) adds a level of intelligence not seen in existing retail systems. Additionally, the solution features a self-adaptive information extraction layer that improves accuracy with each iteration using user feedback.

Feasibility and Manufacturability

The system has been implemented and tested using off-the-shelf deep learning models such as YOLOv11, ResNet-50, and PaddleOCR, making it both hardware-agnostic and scalable. It is deployable on edge devices with GPU support or through cloud APIs, and it requires no specialized sensors beyond a standard camera. The codebase is modular and developed in Python with PyTorch and TensorFlow, ensuring compatibility with existing ML infrastructure. Its cost-effective production leverages open-source libraries, reducing the overall development and deployment cost.

Marketability and Applications

The product has broad applications in sectors such as retail (automated checkout, inventory management), warehousing (expiry-based sorting), and agriculture (freshness-based grading). In e-commerce logistics, it can automate product validation during packaging or delivery. Supermarkets and smart vending machines can benefit from its shelf-monitoring and restocking capabilities. Given the increasing emphasis on product traceability and quality assurance in the FMCG sector, the market potential is substantial. The system can be sold as a SaaS platform or embedded into existing POS and warehouse systems.

In summary, this AI-based product categorization and information extraction system represents a significant step forward in automating retail intelligence with a novel, modular, and scalable architecture.