Handwritten Word Recognition

Handwritten word recognition (HWR) aims to automatically transcribe handwritten text, a challenging task due to variations in writing styles and image quality. Current research heavily utilizes deep learning, particularly Convolutional Neural Networks (CNNs) combined with Recurrent Neural Networks (RNNs), such as BiLSTMs, often incorporating attention mechanisms to improve accuracy. Significant efforts focus on mitigating data scarcity through synthetic data generation and self-training techniques, aiming to improve performance on real-world datasets. Advances in HWR have broad implications for digitizing historical archives, automating document processing, and improving accessibility for individuals with disabilities.