Research on Handwriting Character Recognition Technology Based on Bidirectional Long Short Term Memory Neural Network

Handwritten character recognition has always been a research hotspot in the fields of computer vision and pattern recognition. In recent years, significant progress has been made in this area with the rapid development of deep learning technology, particularly in handwritten character recognition based on neural networks. This paper proposes an improved Bidirectional Long Short-Term Memory Neural Network (BiLSTM) for handwritten character recognition. Firstly, we perform preprocessing, such as normalization, based on the dataset's characteristics. Then, a CNN network is used to extract features from the images. Next, we employ a Bidirectional LSTM network to remember the word-level relationships of the handwritten text sequences and make predictions. Finally, we use the CTC-Loss as the loss function, which enables the training set with full sentence annotations to converge under the aforementioned network. During the training process, the backpropagation algorithm is utilized to optimize the network parameters. Experimental results demonstrate that our proposed BiLSTM-based handwritten character recognition technique achieves the best recognition performance when compared with multiple network models. Our approach exhibits significant improvements in both accuracy and robustness compared to traditional methods.