Handwritten Text Recognition Using Deep learning

Handwritten text recognition (HTR) represents an ongoing field challenge because different writing styles combined with multiple handwriting patterns create vast recognition complexity. The paper presents a new HTR solution that combines Vision Transformers with MobileNet-LSTM hybrid architecture to maximize recognition precision and operational speed. The Vision Transformer demonstrates excellence at capturing long-distance image relationships when extracting meaningful characteristics from handwritten information. MobileNet serves to minimize computational requirements of the system while sustaining high performance standards and the system's temporal dependency skill has been achieved through Long Short-Term Memory (LSTM) network application for sequence modeling in handwriting. Engineered from convolutional neural network (CNN)-based approaches the proposed evaluation method provides better recognition rates on standardized benchmark datasets. Experimental data confirms the ViT and MobileNet-LSTM combination solves real-time handwritten text recognition problems by achieving high accuracy rates with efficient computational requirements.

Keywords: Handwritten Text Recognition, Vision Transformer, MobileNet, LSTM, Deep Learning, Sequence Modeling, Computer Vision, Feature Extraction, Real-Time Recognition.