Point Symbol Recognition Algorithm based on Improved Generalized Hough Transform and Nonlinear Mapping The point symbol recognition in the colorscan topographic map is a difficult task in the pattern recognition of the map, which has the problems of low quality of the scanned topographic map, small symbol area, diversified shapes, superimposition with other topographic elements in the map and difficulty of separation. Therefore, many traditional recognition methods, which usually based on presegmentation and recognition, do not perform well in the face of such a complex condition. In this paper, we present a new method of point symbol recognition based on improved generalized Hough transform and nonlinear mapping. The proposed method contains two steps. At the first step, an improved generalized Hough transform algorithm is used to extract the target symbol from the original topographic map directly. At the second step, through the method of nonlinear mapping and template matching, the preliminary identification of the region for further screening and correction. Experimental results prove that our proposed method can directly recognize and locate point symbols in color topographic maps and outperforms many traditional point symbols recognition algorithms both in both the accuracy of the location of the symbols and the accuracy of the symbol recognition.

HoughNet: Neural Network Architecture for Vanishing Points Detection In this paper we introduce a novel neural network architecture based on Fast Hough Transform layer. The layer of this type allows our neural network to accumulate features from linear areas across the entire image instead of local areas. We demonstrate its potential by solving the problem of vanishing points detection in the images of documents. Such problem occurs when dealing with camera shots of the documents in uncontrolled conditions. In this case, the document image can suffer several specific distortions including projective transform. To train our model, we use MIDV500 dataset and provide testing results. Strong generalization ability of the suggested method is proven with its applying to a completely different ICDAR 2011 dewarping contest. In previously published papers considering this dataset authors measured quality of vanishing point detection by counting correctly recognized words with open OCR engine Tesseract. To compare with them, we reproduce this experiment and show that our method outperforms the stateoftheart result.