Hybrid Differential Evolution Based Neural Network for Improved and Accurate Recognition of Telugu Hand Written Text

Recognition of telugu handwritten texts is challenging because some script has intricate morphology, different salient character combinations and predominant dimensions. Existing techniques methodologies, such as traditional conformist machine learning and deep learning, do not adequately solve these telugu language script problems and usually only provide poor accuracy and with downsize scalability. This paper proposes a hybrid differential evolution neural network that will help to improve ameliorate feature selection and classification of telugu handwritten characters such as foremost compound names such as Guninthamulu and Othulu. The new model takes advantage of kingpin differential evolution's advantages superior to optimize features dynamically, eliminating redundancy and preserving information necessary for accurate recognition. An exhaustive encyclopedic set of 1,924 Telugu characters, comprising different types of writing was created and utilized for the testing of the proposed model. The findings show that the proposed model provides a high consistent accuracy of 98%, far surpassing current methods. The active feature selection system not only increases accuracy, but also boosts the model's scalability to accommodate various handwriting patterns and multidimensional character sets. This research contributes to a revolution in handwritten text recognition for scripts with low representation, and fills in the gap in dataset coverage, feature augmentation, and recognition performance. This model proposed hybrid novel architecture to enhance digital archiving, document digitization, and Optical Character Recognition (OCR) for similarly complex languages. Future work might explore the model's portability to other Indic scripts and further enhance its computation efficiency for large-scale processing.