Novel feature extraction technique for the recognition of handwritten digits

This paper presents an efficient handwritten digit recognition system based on support vector machines (SVM). A novel feature set based on transition information in the vertical and horizontal directions of a digit image combined with the famous Freeman chain code is proposed. The main advantage of this feature extraction algorithm is that it does not require any normalization of digits. These features are very simple to implement compared to other methods. We evaluated our scheme on 80,000 handwritten samples of Persian numerals and we have achieved very promising results

A new algorithm for skew correction and baseline detection based on the randomized Hough Transform

The proposed technique is based on the detection of the lower baselines of the text lines of Arabic documents. As the lower baseline pixels belong to the lower edge of the word images, we first locate vertically the black–white transitions at the black pixels where the resulting image would emphasize the baselines of the text. Once the skew angle is determined using a randomized Hough transform, the baselines are extracted using y-intercept histogram. This algorithm can also contribute significantly for text line extraction from skewed document images for many languages