Review of Artificial Intelligence Methods in Handwriting Identification Using CNN-RNN for Textural Features

This review discusses the developments in handwriting recognition and optical character recognition (OCR) systems, with a focus on the transformative role of artificial intelligence (AI) in enhancing accuracy, precision, and scalability. Modern methods include convolutional neural networks (CNNs), recurrent neural networks (RNNs), hybrid CNN-RNN frameworks, and transformer-based architectures. These methods have been evaluated to address a wide range of tasks including handwritten text recognition (HTR), signature verification (SV), and multilingual text analysis. The impressive results have been achieved across a variety of datasets with 99.98 %-digit recognition and more than 97 % online handwritten word recognition. This involves high-end models such as hybrids CNN-RNN and transformer architecture, which have shown their effectiveness in capturing spatial and temporal features and adapting to handwriting variability in writing and linguistic contexts. The review underlines the importance of texture and structural feature analysis to progress with AIbased handwriting systems while emphasizing the extraction of fine-grained details such as pen pressure and stroke dynamics. Despite these advances, the issue of managing handwriting variability, dynamic styles, and underrepresented scripts like non-Latin languages still persists. Some open areas of research include robustness improvement, fine-tuning models to better adapt to variability, and interdisciplinary applications in forensic analysis, education, and medical diagnostics.