Character recognition in low quality document images using local and global features
Although a great success has been achieved for the situation of high quality images during the past decades, Character recognition in low quality images still remains a challenge. To tackle this challenge, in this paper a novel method in the SVM framework is proposed to recognize the characters in low quality document images by using local and global features. Firstly, a multi scale sliding window strategy with a pruning method of character traits is adopted to generate potential character sub regions. Then, the conventional global feature and state of art local feature, namely histogram of oriented gradients (HOG) are extracted to form the representation of the potential character sub region. Finally, the Support Vector Machine (SVM) is used to recognize characters with a late fusion strategy. Experimental results show that the proposed method has a better performance even in the situation of existing touched and broken characters situation compared to the conventional method.
2014 7th International Congress on Image and Signal Processing