State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture


Authors : Mujadded Al Rabbani Alif

Volume/Issue : Volume 9 - 2024, Issue 1 - January

Google Scholar : http://tinyurl.com/4kecwrcx

Scribd : http://tinyurl.com/y9cf5rv3

DOI : https://doi.org/10.5281/zenodo.10538255

Abstract : Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text.

Keywords : Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.

Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text.

Keywords : Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.

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