Symbol recognition using bipartite transformation distance and angular distribution alignment

Abstract

In this paper, we present an integrated system for symbol recognition. The whole recognition procedure consists of image compres- sion, denoising and recognition. We present a pixel-based method to calculate similarity between two symbols using the bipartite transforma- tion distance after they are aligned by their angular distributions. The proposed method can overcome some shortcomings of other pixel-level methods. We also propose a new denoising technique in our system to improve the recognition precision and efficiency. Evaluation results on test sets provided by the 2nd IAPR contest on symbol recognition show good performance of the system in recognizing symbols with degradation and a±ne transformation.