Review of Artificial Intelligence Methods in Handwriting Identification Using CNN-RNN for Textural Features Chandan Kumar Sonkar School of Computer Science and Engineering Galgotias University Uttar Pradesh c.sonkar1986@gmail.com Dr. Vinod Kumar Computer science and engineering Galgotias University, Greater Noida (U.P.) vinod242306@gmail.com Abstract - 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 AI- based 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. Keywords— Handwriting identification, handwriting verification, artificial intelligence, CNN-RNN integration, textural feature analysis, signature verification, handwritten text recognition, multi-scale analysis, biometric authentication. I. INTRODUCTION Biometric recognition systems, designed to support both identification and verification, employ two types of traits: physical traits, such as fingerprints, DNA, and iris patterns, where the characteristics are relatively stable, and behavioural traits like signatures, gait, and voice, whose characteristic is likely to vary in time and can easily be forged, making precision biometric systems not feasible [1–3]. Among behavioural traits, handwriting is a unique identifier and is sometimes referred to as "brain writing" because it reflects an individual's mood, personality, and motor skills, such as age and nationality [4–5]. The uniqueness of handwriting makes it a very important biometric in forensic analysis, security, and even disease prediction [6]. However, handwriting analysis remains a difficult problem in artificial intelligence, especially in Handwritten Text Recognition (HTR) and Signature Verification (SV). Document Image Analysis is a key area, which is further subdivided into off-line and on-line systems. The former is much more complex because dynamic data such as writing speed and pen pressure are absent. Applications such as HTR, which converts handwritten documents into symbolic representations, and SV, which authenticates signatures, point out the high interpersonal and intrapersonal variability in handwriting, which makes analysis even more difficult [7–8]. This is necessary in improving accuracy as sources of errors could be those as depicted in Fig. 1- misaligned strokes, unusual styles, among others. Fig. 1. Examples for sources of error with resulting incorrect embeddings: a, b unaligned delayed strokes; c unusual writing style; d incorrect type detection. The original writings are given in the first column [59] Current AI has fundamentally transformed handwriting analysis so that identification and verification can be accelerated further. Starting from the pace and accuracy levels much quicker than in manual examination; thus, a much faster and scalable process than what is possible with manual examination, which is extremely time-consuming and error- prone. As AI exploits the superiority of machine and deep learning techniques, such as Convolutional Neural Networks and Recurrent Neural Networks, complex features are automatically extracted to improve HTR and SV tasks [9]. AI systems now are able to differentiate between different forms of handwriting and cope with vagaries in both printed and hand-written documents and pictures, which traditional methods found very difficult to handle. AI-based hand-writing verification systems have serious practical applications for forensic investigation, banking, and security and provide greater precision and less numbers of false positive in writing authentication [10]. This study briefly outlines the role of AI in handwriting recognition, focusing on the integration of 648 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) 979-8-3315-1852-3/25/$31.00 ©2025 IEEE 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) | 979-8-3315-1852-3/25/$31.00 ©2025 IEEE | DOI: 10.1109/IC3ECSBHI63591.2025.10990766 Authorized licensed use limited to: University of Delhi. Downloaded on May 26,2025 at 12:28:18 UTC from IEEE Xplore. Restrictions apply.