A Three-Stage Uyghur Recognition Model Combining the Attention Mechanism and Different Convolutional Recurrent Networks

Abstract

Uyghur text recognition faces several challenges in the field due to the scarcity of publicly available datasets and the intricate nature of the script characterized by strong ligatures and unique attributes. In this study, we propose a unified three-stage model for Uyghur language recognition. The model is developed using a self-constructed Uyghur text dataset, enabling evaluation of previous Uyghur text recognition modules as well as exploration of novel module combinations previously unapplied to Uyghur text recognition, including Convolutional Recurrent Neural Networks (CRNNs), Gated Recurrent Convolutional Neural Networks (GRCNNs), ConvNeXt, and attention mechanisms. Through a comprehensive analysis of the accuracy, time, normalized edit distance, and memory requirements of different module combinations on a consistent training and evaluation dataset, we identify the most suitable text recognition structure for Uyghur text. Subsequently, utilizing the proposed approach, we train the model weights and achieve optimal recognition of Uyghur text using the ConvNeXt+Bidirectional LSTM+attention mechanism structure, achieving a notable accuracy of 90.21%. These findings demonstrate the strong generalization and high precision exhibited by Uyghur text recognition based on the proposed model, thus establishing its potential practical applications in Uyghur text recognition.