Advanced Methods for Text Detection and Recognition in Customs Declaration Forms: The EFF-DBNet and SKSVTR

Customs documents contain a large amount of information. The image quality of customs declaration forms is often poor, with issues such as blurred characters, missing strokes, stroke merging, and noise contamination, which increase the difficulty of detection and recognition. Previous approaches for text detection and recognition have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with customs declaration forms. In this paper, we propose EFF-DBNet, an efficient text detector, and SKSVTR, an advanced text recognition model. In the detection stage, we redesign the network based on the DBNet. We introduce the Efficient Channel Attention to extract features from image and utilize Feature Pyramid Enhancement Modules (FPEMs) for feature enhancement, then use a Feature Fusion Module to gather the features given by FPEMs of different depths into a final feature. For the recognition stage, we improve the SVTR model by replacing the local mixing in the backbone network with convolutional modules, and then introduce the Selective Kernel Attention to effectively capture multi-scale features in the image space. Experimental results on customs declaration forms demonstrate the effectiveness of our methods, which outperform existing methods.