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Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network.

Mvv Prasad Kantipudi1, Sandeep Kumar2, Ashish Kumar Jha3

  • 1Department of E&TC, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.

Computational Intelligence and Neuroscience
|December 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for scene text recognition, combining deep convolution neural networks (CNN) and bidirectional LSTM (Bi-LSTM). The approach achieves high accuracy, improving upon existing algorithms for image-based text identification.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Scene text recognition is crucial for many AI applications.
  • Existing algorithms struggle with accuracy and speed in complex visual scenes.
  • Deep learning offers potential for improved text recognition capabilities.

Purpose of the Study:

  • To propose a novel approach for enhanced scene text recognition.
  • To improve the accuracy and efficiency of text identification in images.
  • To integrate deep convolution neural networks (CNN) and bidirectional Long Short-Term Memory (Bi-LSTM) for scene text recognition.

Main Methods:

  • A contour-based image processing technique is applied to identify image contours.
  • Deep convolution neural networks (CNN) extract sequential features from the contoured images.
  • Bidirectional Long Short-Term Memory (Bi-LSTM) networks encode these features for recognition.

Main Results:

  • The proposed method achieved high accuracy rates across multiple datasets: MSRATD 50 (95.22%), SVHN (92.25%), vehicle number plate (96.69%), SVT (94.58%), and random datasets (98.12%).
  • Quantitative and qualitative analyses confirm the approach's superior performance.
  • The integration of CNN and Bi-LSTM with contour-based input enhances recognition speed and accuracy.

Conclusions:

  • The novel deep learning approach significantly improves scene text recognition.
  • The combined CNN and Bi-LSTM model offers a promising solution for accurate and efficient text identification in diverse image scenarios.
  • This method represents a significant advancement over existing scene text recognition techniques.