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.
