| Title |
Design of a Poster Visual Communication Text Information Extraction System Based on Semantic Segmentation and CNN |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.3.323 |
| Keywords |
Text detection; Text recognition; Semantic segmentation; Convolutional neural network; Sequence encoding |
| Abstract |
Due to Internet and information technology developing, the rapid expansion of poster advertising brought about by online e-commerce has also posed a challenge to its standardized application and review work. Research has optimized character detection, text recognition, and keyword extraction in optical character recognition systems to reduce audit costs and improve work efficiency. A convolutional semantic segmentation network is used to detect text images and establish semantic segmentation channels to fuse semantic information, thereby achieving image segmentation accuracy. To address the complex background of text recognition, a multi-scale sequence encoding recognition algorithm with attention was designed by combining the two-dimensional spatial features of images and the sequence characteristics of text. And experiments were conducted on different algorithms and types of information, and good recognition accuracy rates were obtained, which were 86%, 89%, and 93%, respectively. The recognition rates for different types are 97.7%, 98.5%, 98.9%, and 98.6%, respectively. Finally, model operations were carried out on the server and client sides in the operational framework of the system. These experimental results confirm the superior performance of the multi-scale sequence encoding model based on semantic segmentation and attention mechanism. This provides a theoretical basis and technical reference for the recognition and shielding of printed media text information. |