AI/ML used for shelf label information extraction in Mobile App using Tensorflow

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.

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80% accuracy in data capture, seamlessly stock management happen

AI Capabilities

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.

  • On-device object detection using TensorFlow to accurately identify shelf labels and price regions.
  • AI-powered OCR for precise extraction of product names, prices, and other label attributes.
  • Offline-first inference ensuring data privacy and uninterrupted functionality.
  • Intelligent image preprocessing and region cropping to improve OCR accuracy.
  • Structured data generation in JSON format with built-in validation and error handling.
  • Reusable AI/ML SDK for seamless integration into existing mobile applications.
  • Scalable architecture supporting high-volume image processing with consistent performance.
Innovation Begins Here

AI Benefits

  • Automated Data Capture โ€“ Instantly extracts product names, prices, and label details directly from shelf images.
  • Reduces Manual Entry Effort โ€“ Eliminates time-consuming manual data recording and minimizes human errors.
  • Improves Pricing Accuracy โ€“ Ensures structured and validated data for reliable inventory and pricing updates.
  • Offline-First & Privacy-Focused โ€“ Processes data directly on the device without mandatory internet connectivity.
  • AI-Powered Precision โ€“ Combines TensorFlow-based object detection and OCR for accurate label recognition.
  • Structured JSON Output โ€“ Generates clean, validated, integration-ready data with built-in error handling.
  • Seamless App Integration โ€“ Reusable AI/ML SDK easily integrates into existing Android and iOS applications.
  • Scalable for High Volume โ€“ Designed to handle large-scale retail environments with consistent performance.
  • Enhances Inventory Management โ€“ Enables faster stock updates and better operational efficiency.
  • Future-Ready Architecture โ€“ Built with modern AI/ML frameworks to support continuous improvement and expansion.
Innovation Begins Here

Programming Languages

Core languages for AI and ML, optimized for performance and scalability.

  • Python

  • TensorFlow Lite

  • YOLO

  • Java

  • Objective C

  • Swift

Integrates With Your Existing Tools

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.

    • This tool is seamlessly integrated with the clientโ€™s existing application.

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Frequently asked questions

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:

  • Python
  • TensorFlow Lite
  • YOLO (Object Detection)
  • AI-based OCR
  • Java (Android)
  • Objective-C / Swift (iOS)

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.

  • Reduces manual data entry effort
  • Improves pricing accuracy
  • Enhances inventory management
  • Supports privacy-preserving on-device processing
  • Enables faster stock updates and automation

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