Design a Model for Handwritten Text Recognizing System Using Deep Learning

Abstract

newlineHandwritten text recognition (HTR) is a challenging task in computer vision and pattern recognition, which involves converting images of handwritten text into machine-readable text. In recent years, deep learning methods have been widely used to improve the performance of HTR systems. One of the most popular deep learning architectures for HTR is the convolutional neural network (CNN) combined with a gated recurrent unit (GRU) network. newlineThe proposed methodology for HTR using a CNN and GRU hybrid method involves pre-processing the input images to extract useful features, followed by a CNN-based feature extraction step. The extracted features are then fed into a GRU-based sequence recognition network to predict the corresponding text. The GRU network is able to capture the temporal dependencies between the features extracted by the CNN, which improves the recognition accuracy. newlineThe proposed method has been evaluated on three character datasets Char74k dataset, MNIST and Devnagari Character datast and Three Text Dataset such as the IAM dataset, Washington Dataset and Saintgall dataset and the results have shown that it outperforms other state-of-the-art HTR methods in terms of recognition accuracy and Loss. The use of the hybrid CNN-GRU architecture also reduces the number of parameters in the model, which leads to faster training and inference times. CNN and GRU approach for Handwritten Text Recognition (HTR) is a popular method for converting images of handwritten text into machine-readable text. The approach uses a combination of a convolutional neural network (CNN) and a gated recurrent unit (GRU) network to model the visual features and temporal dependencies of the handwritten text, respectively. newlineThe CNN is used to extract features from the input images, which are then passed to the GRU network for sequence recognition. The CNN is trained to learn a feature representation of the input images that is robust to variations in handwriting, such as different writing styles, sizes, and orientations. newlineThe