Multiscale Residual Network for Recognizing Handwritten Malayalam Characters

In domains such as banking cheque processing and automated mail sorting, the recognition of handwritten characters is of paramount importance. In Kerala, where Malayalam serves as the primary language for government documentation, the accurate identification of its handwritten characters is crucial. This study introduces a novel approach leveraging a deep residual neural network with multiscale feature extraction for the recognition of Malayalam handwritten characters, encompassing both basic and compound characters as well as signs. Traditional methods of character recognition often rely on handcrafted feature extraction, which, while achieving commendable accuracy, are prone to misclassification due to reliance on low- and mid-level features in the output layer classifier without consideration of parameter modifications. The proposed method addresses these limitations by integrating multiscale features, enhancing the model's ability to discern intricate character details. Evaluated using the P-ARTS Kayyezhuthu Dataset, this approach demonstrated a remarkable accuracy of 99.56%. Additionally, a commendable accuracy of 98% was achieved on other test datasets. The findings underscore the efficacy of deep learning techniques over conventional methods in handwritten character recognition (HCR), particularly in the context of the complex Malayalam script. This study contributes significantly to the field of machine learning and handwriting analysis, offering robust solutions for applications requiring high precision in character recognition.