Research and implementation of Japanese multimodal translation system based on computer vision

This paper studies and implements a Japanese multimodal translation system combined with computer vision, aiming at solving the limitations of traditional text translation technology in dealing with multimedia information including images and videos. The unique grammatical structure and writing system of Japanese bring challenges to multimodal translation. Therefore, this study proposes an improved CRNN-OCR model based on deep learning. By introducing sequential attention mechanism and dynamic ROI pooling technology, the recognition rate of Japanese characters in complex backgrounds and diverse fonts is significantly improved. At the same time, a multi-modal fusion algorithm is designed, which combines image visual information with text semantic information by using cross-modal feature fusion strategy, and enhances the model's ability to understand complex scenes. In view of the grammatical structure differences between Japanese and Chinese, this paper puts forward a word order adjustment strategy based on dependency syntactic analysis, which effectively improves the fluency and accuracy of translation results. The system adopts a three-stage cascade architecture, including image preprocessing, optical character recognition (OCR) recognition, multimodal fusion, translation generation and target language output. The experimental results show that the system is superior to the existing technology in OCR performance and translation quality, and the accuracy of characters and the BLEU-4 score of translation are significantly improved. At the same time, it also performs well in real-time, and the total time consumption of end-to-end processing is significantly reduced.