Text Spotting of Electrical Diagram Based on Improved PP-OCRv3

The text detection and recognition plays an important role in automatic management of electrical diagrams. However, the images of electrical diagrams often have high resolution, and the format of the text in them is also unique and densely distributed. These factors make the general-purpose text spotting models unable to detect and recognize the text effectively. In this paper, we propose a text spotting model based on improved PP-OCRv3 to achieve better performance on text spotting of electrical diagrams. Firstly, a region re-segmentation module based on pixel line clustering is designed to correct detection errors on irregularly shaped text containing vertical and horizontal characters. Secondly, an improved BiFPN module with channel attention and depthwise separable convolution is introduced during text feature extracting to improve the robustness of input images with different scales. Finally, a character re-identification module based on region extension and cutting is added during the text recognition to reduce the adverse effects of simple and dense character on the model. The experimental results show that our model has better performance than the state-of-the-art (SOTA) methods on the electrical diagrams data sets.