OCRNet - Light-weighted and Efficient Neural Network for Optical Character Recognition

The developing expertise of neural networks has validated extraordinary outcomes within text detection. The study seeks to enhance the accuracy of textual content identity to improve the existing technology. Two numbers, one additive, text detection, and textual content reputation are used to identi...

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Bibliographic Details
Published in2021 IEEE Bombay Section Signature Conference (IBSSC) pp. 1 - 4
Main Authors Gupta, Vansh, Gupta, Ayush, Arora, Nikhil, Garg, Jai
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.11.2021
Subjects
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computer vision
Deep learning
Neural networks
Optical charter recognition
Optical computing
Optical distortion
Scene Text detection
Shape
Supervised learning
Text recognition
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Summary:The developing expertise of neural networks has validated extraordinary outcomes within text detection. The study seeks to enhance the accuracy of textual content identity to improve the existing technology. Two numbers, one additive, text detection, and textual content reputation are used to identify the optical character. This paper provided a way to determine the degree of similarity between every unique character, in order, that each word may also in the end to be diagnosed. Extensive testing of two datasets,TotalText and CTW-1500, indicates that the optical character detection at character level outplays State of the Art. According to findings, this endorsed technique assures that complex textual content pix, which include letters randomly orientated, bent, or distorted, would be recognized as being very adaptable.
DOI:10.1109/IBSSC53889.2021.9673254