A Locale Group Based Line Segmentation Approach for Non Uniform Skewed and Curved Arabic Handwritings

In this paper we present a novel local group based method for extracting skewed and curved handwritten text lines in Arabic document images. We first detect all connected components and use a Support Vector Machine (SVM) to classify them either as Piece of Arabic Word (PAW) or diacritic. We then, use novel distance measures like sigmoid function based shapes to calculate the nearest neighbors for all PAWs. A subsequently graph based grouping algorithms, which follows the text lines from right to left, generates multiple candidate lines. After assessing the quality of all line candidates the final line representation is chosen. In a final step all PAWs which are not already part of a final line are inserted into the one that is closest. Experimental results show a successfully line segmentation for documents of different writers and styles.