Abstract
In the field of computer vision and document analysis, the identification and categorization of Indian scripts in natural scene images pose a difficult yet crucial challenge. The variety of characters and intricate writing styles in Indian scripts require reliable solutions for precise identification under different environmental conditions. This study presents a novel CNN model designed for identifying scripts in Indian multilingual document images captured by cameras. Experimental evaluations of the model's performance were conducted with two regional languages (Odia and Telugu) and one national language (Hindi). The average accuracy in script recognition for the three language combinations is 95.66%, with Odia achieving 99.00%, Hindi 90.33%, and Telugu 98.12%. The model achieved the highest accuracy in recognition. The model achieved the highest accuracy in recognition Keywords: Text Recognition, Image Augmentation, CNN, LSTM, VGG, ResNet, DenseNet, Datasets, Natural Images
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