JOURNAL ARTICLE

Bangla Handwritten Character Recognition using Convolutional Neural Network

Abstract

Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters.Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla.Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes.Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition.In this paper, a CNN based Bangla handwritten character recognition is investigated.The proposed method normalizes the written character images and then employ CNN to classify individual characters.It does not employ any feature extraction method like other related works.20000 handwritten characters with different shapes and variations are used in this study.The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.

Keywords:
Computer science Bengali Character (mathematics) Convolutional neural network Intelligent word recognition Artificial intelligence Character recognition Pattern recognition (psychology) Feature (linguistics) Feature extraction Neocognitron Speech recognition Intelligent character recognition Artificial neural network Natural language processing Image (mathematics) Mathematics Linguistics

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121
Cited By
6.26
FWCI (Field Weighted Citation Impact)
14
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0.97
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Citation History

Topics

Handwritten Text Recognition Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Vehicle License Plate Recognition
Physical Sciences →  Engineering →  Media Technology
Hand Gesture Recognition Systems
Physical Sciences →  Computer Science →  Human-Computer Interaction
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