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OCRNet a robust deep learning framework for alphanumeric character recognition to assist the visually impaired.

Aishwarya Nagasubramanian1, Abdulaziz S Almazyad2, Sakthi Abirami Balakrishnan3

  • 1Department of ECE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India. n_aishwarya@ch.amrita.edu.

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This study introduces OCRNet, an AI-powered system for real-time text recognition. This assistive technology aids visually impaired individuals by converting text to speech, enhancing interaction with their environment.

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Gated recurrent unitOptical character recognitionRaspberry PiText-to-speechVisually impaired

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Optical Character Recognition (OCR) systems are crucial AI tools for converting text into machine-readable formats.
  • These systems serve as vital assistive technology for visually impaired individuals, enabling real-time text recognition and environmental interaction.

Purpose of the Study:

  • To present OCRNet, a novel deep learning approach for detecting and recognizing alphanumeric characters in dynamic environments.
  • To develop an efficient and portable OCR system tailored for visually impaired users.

Main Methods:

  • Designed a 43-layer optimized neural network for spatial feature extraction of alphanumeric characters.
  • Integrated a Gated Recurrent Unit (GRU) to capture temporal dependencies, enhancing feature learning.
  • Implemented and tested the hybrid model on a Raspberry Pi platform for portability and affordability.

Main Results:

  • Achieved high performance metrics: 95% accuracy, 94% precision, 95% recall, and 96% F1-score.
  • Demonstrated a fast inference time of 120ms on the Raspberry Pi, ensuring real-time processing.
  • The system provides real-time text recognition with audio feedback for seamless user interaction.

Conclusions:

  • OCRNet represents a robust and efficient deep learning solution for alphanumeric character recognition.
  • The system's performance surpasses current state-of-the-art Convolutional Neural Networks (CNNs).
  • The developed OCR system significantly enhances accessibility for visually impaired individuals, facilitating interaction with various text-based environments.