Enhanced Handwritten Text Recognition through Bidirectional LSTM and CNN Fusion

Abstract: Handwritten Text Recognition (HTR) plays a pivotal position in digitizing historical files, automatic shape processing, and improving accessibility for the visually impaired. This studies paper proposes a singular technique for HTR through integrating Bidirectional Long Short-Term Memory (BiLSTM) networks with Convolutional Neural Networks (CNNs). The fusion of these two architectures harnesses the spatial hierarchies captured via CNNs and the sequential dependencies learned by way of BiLSTM networks, thereby enhancing the version's ability to decipher handwritten textual content. The proposed method is evaluated on well-known benchmark datasets and achieves cutting-edge overall performance in phrases of accuracy and robustness.