A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Abstract

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition at different scales. Further, the previously generated feature map is passed through a head network to produce two maps: a probability map and an adaptive threshold map of the same size as the original map. These maps are then subjected to a differentiable binarization process, resulting in an approximate binarization map. This map helps to identify the boundaries of the text boxes. Finally, the text detection box is generated after the post-processing stage. In the recognition stage, an improved lightweight MobileNetV3 framework is used to recognize the detect character regions, where the original Squeezeand-Excitation (SE) block is replaced by the efficient Shuffle Attention (SA) that integrates spatial and channel attention, improving the accuracy of Yi characters recognition. Meanwhile, the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model, so that the model can better understand the contextual information and improve the accuracy of text recognition. The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters, with detection and recognition accuracy rates of 97.5% and 96.8%, respectively. And also, we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms. In these comparisons, the proposed model achieves better detection and recognition results with a certain reliability.