Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Latent Style Model: Discovering writing styles for calligraphy works
Journal of Visual Communication and Image Representation
Offline handwritten arabic character segmentation with probabilistic model
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Segmentation of Arabic Characters: A Comprehensive Survey
International Journal of Technology Diffusion
| Hi-index | 0.00 |
In this paper, we propose two methods of character segmentation for Arabic handwritten characters and cursive Latin characters. Classical horizontal and vertical projections detect the lowercase writing area in lines. The problem of overlapping lower or upper strokes is resolved with a contour-following algorithm which starts in the lowercase writing area and labels the detected contours. In the first method, the junction segments connecting the characters to each other are detected by taking into account the writing line thickness. The second method detects the upper contour of each word. The strokes are detected in order to find primary segmentation points (PSP). These points are analysed with an automaton that considers the shape of the word for the determination of definitive segmentation points (DSP). The two methods are compared and the results are discussed.