AILS: Experimental Evaluation of Handwritten Text Character Recognition System using Artificial Intelligence based Learning Scheme

Handwritten text recognition (HTR) is a critical task with applications in document digitization, automated transcription, and more. This paper explores the integration of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Connectionist Temporal Classification (CTC) for HTR, with a focus on implementation using Python. Beginning with preprocessing to standardize input data and convert labels to numerical values, the methodology progresses through CNN feature extraction and RNN sequence modeling phases. The integration of the CTC loss layer enables end-to-end training and handling of variable-length sequences. Results show an impressive accuracy of 95%, showcasing the effectiveness of the proposed approach. Comparison tables further illustrate performance across various techniques, models, configurations, datasets, hardware setups, and noise levels. Through rigorous implementation and evaluation, this methodology demonstrates its potential for accurate handwritten text transcription, promising valuable applications in diverse real-world scenarios