Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning

Abstract

AbstractHandwritten recognition is a difficult task. The conventional technique relies on the character segmentation, feature extraction, and classification process. The segmentation is a tremendous challenge when there are variation of character patterns and alignments in a sentence, such as linking segments between characters in the Thai language. Promising segmentation outcome is favorable but not applicable in most applications. This work proposes a methodology for Thai handwritten recognition by applying Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The first step is text localization before feeding to the network. CNN extracts the abstract features before they are fed to RNN to learn the sequence of characters in an image. The optimization is performed with an integrated Connectionist Temporal Classification (CTC) module (to arrange the final results). A standard Thai handwritten dataset (BEST2019) and more collection are used in this study. for training and test sets. The experimental results show that the integration of CNN and RNN provides promising results of the test set with a Character Error Rate (CER) of 1.58%. For testing with the seen and unseen dataset of the final round of BEST2019 competition, the CER is at 24.53%.KeywordsDeep learningConvolutional Neural NetworkRecurrent Neural NetworkHandwritten recognition