%0 Conference Paper %1 Ozdil1997 %A Ozdil, M.A. %A Vural, F.T.Y. %B Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on %D 1997 %I IEEE Computer Society %K (artificial algebra, archive, attribute character control cursive extraction extraction, feature group, image intelligence), learning matching matching, matrix matrix, measures, mode, off-line optical overlap point position probability probability, process, quality, recognition, script, segmentation-free set, training vectors, vectorsalphabet, word %P 483--486 %R 10.1109/ICDAR.1997.620545 %T Optical character recognition without segmentation %V 2 %X A segmentation-free approach for off-line optical character recognition is presented. The proposed method performs the recognition by extracting the characters from the whole word, avoiding the segmentation process. A control point set which includes position and attribute vectors is selected for the features. In the training mode, each sample character is mapped to a set of control points and is stored in an archive which belongs to an alphabet. In the recognition mode, the control points of the input image are first extracted. Then, each control point is matched to the control points in the alphabet according to its attributes. During the matching process, a probability matrix is constructed which holds some matching measures (probabilities) for identifying the characters. Experimental results indicate that the proposed method is very robust in extracting the characters from a cursive script

@inproceedings{Ozdil1997, abstract = {A segmentation-free approach for off-line optical character recognition is presented. The proposed method performs the recognition by extracting the characters from the whole word, avoiding the segmentation process. A control point set which includes position and attribute vectors is selected for the features. In the training mode, each sample character is mapped to a set of control points and is stored in an archive which belongs to an alphabet. In the recognition mode, the control points of the input image are first extracted. Then, each control point is matched to the control points in the alphabet according to its attributes. During the matching process, a probability matrix is constructed which holds some matching measures (probabilities) for identifying the characters. Experimental results indicate that the proposed method is very robust in extracting the characters from a cursive script}, added-at = {2011-03-27T19:35:34.000+0200}, author = {Ozdil, M.A. and Vural, F.T.Y.}, biburl = {https://www.bibsonomy.org/bibtex/204a9ab012efec7f898d7389e3d081c1c/cocus}, booktitle = {Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on}, booktitleaddon = {Aug 18-20, 1997}, doi = {10.1109/ICDAR.1997.620545}, file = {:./00620545.pdf:PDF}, interhash = {db1e2ec0fdb79073abb75de8b2ea32c6}, intrahash = {04a9ab012efec7f898d7389e3d081c1c}, keywords = {(artificial algebra, archive, attribute character control cursive extraction extraction, feature group, image intelligence), learning matching matching, matrix matrix, measures, mode, off-line optical overlap point position probability probability, process, quality, recognition, script, segmentation-free set, training vectors, vectorsalphabet, word}, location = {#ieeeaddr#}, pages = {483--486}, publisher = {{IEEE} Computer Society}, timestamp = {2011-03-27T19:35:42.000+0200}, title = {Optical character recognition without segmentation}, volume = 2, year = 1997 }