CNNGRU Model for Handwritten Character Recognition and Evaluating its Performance on the Char74k Dataset
Article No.: 37, Pages 1 - 7
Abstract
Recently, advancements in artificial intelligence and computer vision have led to the emergence of novel character recognition methods. While humans excel at recognizing characters, objects, and individuals with remarkable precision, machines encounter difficulties, especially in discerning diverse patterns and shapes of characters across various languages. To aid machines in character recognition, researchers have employed a range of techniques, such as scrutinizing input images, pinpointing distinctive features, implementing classification, and training neural networks. This study introduces an innovative hybrid approach to character recognition that surpasses previous methodologies. This hybrid model seamlessly integrates a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) to enhance both accuracy and processing speed. The GRU takes the place of the fully connected layer in the CNN, dynamically classifying the extracted characteristics from the input image. The model is assessed using the Char74k dataset, achieving a training accuracy of 99.48% and a validation accuracy of 95.21%.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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