## 2 Related Work  
### Public Opinion Scene Text Recognition  
Text recognition is one of computer vision's most active areas of study [12]. Early Europe and the United States utilized the method to process newspapers, periodicals, data reports, etc. Our text recognition methods have evolved from traditional text recognition methods to mainstream deep learning text recognition methods [13; 14] and multimodal text recognition methods in more cutting-edge disciplines [15; 16] from the 1970s to the present. Text detection and recognition consist of three primary tasks: text detection, text recognition, and end-to-end recognition. In terms of text irregularities in visual images, text distortion, multiple scales, text recognition accuracy, and speed, etc., exist. The dominant deep learning text recognition methods have made significant progress. However, as the requirements for text recognition tasks become more diverse, emerging multimodal text recognition methods become more difficult. The traditional text recognition method employs a manual feature extraction technique, which is simple to comprehend but lacks generalizability. The deep learning text recognition method uses a convolutional neural network method which improves the neural network text recognition results through deep learning attention. This improves the accuracy and robustness of text recognition, making it better than the traditional text recognition method. With technological developments in single-modal AI technology, there is a developing desire among academics for multimodal information technology. Methods for multimodal text recognition attempt to combine visual feature information such as image, video, audio, etc., first through translation [17] and alignment [18] of modal information. The model is then pretrained to facilitate cross-modal interaction between features to learn about the concealed wealth of information between modalities. Finally, the model can accurately extract the information of characters and fields, and it has a more pronounced effect enhancement in generalization ability and scene adaptation, making it superior in scene application to the deep learning text recognition method. For multimodal text recognition, there are still many issues that require further investigation. This chapter examines the deficiencies of the text detection and recognition modules in multimodal scenes. In the detection module, we proposed the LK-PAN network of large receptive fields to improve the CML distillation strategy and then employed an RSE-FPN with a residual attention mechanism. Experimental verification has shown that it can perform well in detecting large font text and extreme aspect ratio text in image and video datasets. In the recognition module, we modified the original CTC decoder to use the GTC method and applied a sinusoidal loss function for rotation recognition. This loss function can optimize the model's recognition of rotating fonts and improve text detection performance in settings with arbitrary rotation angles. Following validation on image and video datasets, the detection and recognition performance of arbitrarily rotated text has been enhanced to some degree. In two sections, Sections 2.1.1 and 2.1.2, the research related to text detection and recognition is elucidated.