An AI/ML-based image processing solution to automatically extract structured product and label information from images captured via a mobile application. The solution leverages on-device AI/ML models, OCR, and a reusable SDK to deliver accurate, scalable, and offline-capable product label extraction.
80% accuracy in data capture, seamlessly stock management happen
The solution automates the extraction of product and label details directly from images, significantly reducing manual effort while improving overall data accuracy. It supports offline processing to ensure uninterrupted functionality, with seamless synchronization once connectivity is restored, and includes a reusable SDK that allows easy integration with existing mobile applications.
This pipeline delivers fast, reliable, and privacy-preserving inference by operating entirely without mandatory network dependency. Images are first captured through the device camera and passed to an object detection model, which identifies and localizes price tags and label regions within the image. The detected regions are then automatically cropped to isolate only the relevant areas, improving accuracy and reducing noise. These cropped images are processed using AI-based OCR to extract textual information such as product names, prices, and weights, which is finally converted into structured JSON output, with robust error handling in place for unreadable or incomplete data.
Our solution leverages advanced AI and machine learning to deliver accurate and reliable shelf label recognition. Using on-device object detection with TensorFlow and AI-powered OCR, it precisely identifies price regions and extracts product details such as names and prices. The system operates with offline-first inference to ensure data privacy and uninterrupted performance, while intelligent image preprocessing enhances accuracy. It generates structured, validated JSON data for seamless integration and offers a reusable AI/ML SDK designed to scale efficiently for high-volume image processing across mobile applications.
Core languages for AI and ML, optimized for performance and scalability.
The AI/ML used for shelf label information extraction in Mobile App using Tensorflow Tool integrates seamlessly with popular content management and marketing platforms, fitting easily into existing business workflows.
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Art Gallery Center
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Adam
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Greg Myroth
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Franz
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Upwork enterprise client
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Gornji Milanovac
Get quick answers to common questions and find helpful information to resolve your queries.
This is an AI/ML-powered image processing solution designed to automatically extract structured product and pricing information from shelf label images captured via a mobile application. It leverages on-device models, OCR, and a reusable SDK to deliver fast, scalable, and offline-capable data extraction.
The process begins when an image is captured through the mobile device camera. An on-device object detection model (using TensorFlow Lite and YOLO) identifies and localizes shelf labels and price regions. These regions are automatically cropped and processed using AI-powered OCR to extract text such as product names, prices, and weights. The extracted data is then converted into structured JSON format with built-in validation and error handling.
Yes. The system follows an offline-first architecture, allowing all AI inference and OCR processing to occur directly on the device without mandatory network connectivity. Data can be synchronized seamlessly once connectivity is restored.
The solution achieves up to 80% accuracy in data capture, significantly reducing manual entry errors and improving stock management efficiency.
The system is built using:
The extracted information is converted into structured JSON output, including product names, prices, weights, and other label attributes. The output includes built-in validation and error handling for incomplete or unreadable data.
Yes. The solution includes a reusable AI/ML SDK that can be seamlessly integrated into existing Android and iOS applications without major architectural changes.
Absolutely. The architecture is designed to support high-volume image processing with consistent performance, making it suitable for retail chains and large-scale stock management operations.
Duration
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