Abstract: Due to the wide variety of writing styles, the recognition of handwritten characters, particularly in Kannada, is a challenging area of study. In order to convert the handwritten text into an electronic format, this work focuses on deciphering handwritten Kannada characters utilizing deep learning algorithms and optical character recognition (OCR). Deep learning methods like recurrent neural networks (RNNs) and convolutional neural networks are the most renowned and widely used methods for handwriting recognition (CNNs). The paper examines the key algorithms for identifying and categorizing handwritten characters as well as the numerous approaches utilized for deciphering handwritten material. Towards the conclusion, the accuracy offered by various systems is contrasted. In general, technological developments have made life simpler, and the expanding interest in handwritten recognition in computer science follows this trend.
Keywords: Image Processing, CNN, deep learning, handwritten text, and classification.
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DOI:
10.17148/IJARCCE.2023.124165
