Improvement in Efficiency of The State-Of-The-Art Handwritten Text Recognition Models

Abstract

In the past few years, the research in the discipline of Handwritten Text Recognition (HTR) has been fast-tracked as many researchers in computer vision are pursuing this discipline. Most of the deep learning models are likely to have vanishing gradient errors when processing paragraph images like scanned images. The most crucial problem with these models is that they have many parameters, which require a large amount of data and resources. So, the most recent offline HTR follows the Convolutional Recurrent Neural Network (CRNNs). Recently developed neural network architecture was used to get better results, namely Gated Convolutional Neural Network (Gated-CNN), which has fewer layers and parameters. The HTR based on Gated-CNN can outperform the CRNN based HTR. This research surpasses the state-of-the-art HTR system on five different handwritten datasets: Bentham, IAM, RIMES, Saint Gall, and Washington. This research needs low computational resources to use in real life, such as smartphones and robots.