Intelligent feed-optimized convolutional neural network for handwritten character recognition

Computer vision Character recognition is a basic activity used in document digitization, multilingual OCR, and autonomous environment applications. Although conventional approaches have common difficulties with different fonts, linguistic differences, and distortions caused by the environment, deep learning, especially Convolutional Neural Networks (CNNs), has transformed precision and effectiveness of text recognition by images. Indian scripts such as Kannada and Telugu are, however, special concerning the curvilinear characters, complex ligature, different font usage, and noise and distortion in the actual imagery. We will solve these problems by suggesting Intelligent Feed-Optimized CNN model, that is specific to the strong character of classifying Kannada and Telugu characters by images. The model is based on the end-to-end design that does not require manual feature extraction by means of learning spatial features on the basis of raw pixel data. The model uses optimization methods (including batch normalization, regularization in the form of dropouts, adaptive learning rates schedule, and intelligent tuning of the feed) to improve the convergence rate and performance of the recognition. The suggested network is based on the convolutional, pooling, and fully connected layers and has a high accuracy in both the benchmark Kannada and Telugu datasets. The model outperforms conventional CNN models, showcasing strong generalization and adaptability. This scalable and lightweight framework holds practical utility for bilingual applications such as OCR systems, digital archiving, multilingual document processing, and automated text extraction in smart systems.