Segmentation of connected handwritten numeral strings Export

@article{citeulike:820301, abstract = {A new approach to separating single touching handwritten digit strings is presented. The image of the connected numerals is normalized, preprocessed and then thinned before feature points are detected. Potential segmentation points are determined based on decision line that is estimated from the deepest/highest valley/hill in the image. The partitioning path is determined precisely and then the numerals are separated before restoration is applied. Experimental results on the NIST Database 19, CEDAR CD-ROM and our own collection of images show that our algorithm can get a successful recognition rate of 96\%, which compares favorably with those reported in the literature.}, author = {Elnagar, Ashraf and Alhajj, Reda}, citeulike-article-id = {820301}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0031-3203(02)00097-3}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V14-4771RWD-3/2/7cbc7f3ab0340029eb6aaaff3e9693b8}, doi = {10.1016/S0031-3203(02)00097-3}, journal = {Pattern Recognition}, keywords = {handwritten, segmentation, strings}, month = {March}, number = {3}, pages = {625--634}, posted-at = {2006-08-29 02:29:53}, priority = {5}, title = {Segmentation of connected handwritten numeral strings}, url = {http://dx.doi.org/10.1016/S0031-3203(02)00097-3}, volume = {36}, year = {2003} }

TY - JOUR ID - citeulike:820301 L3 - citeulike-article-id:820301 N2 - A new approach to separating single touching handwritten digit strings is presented. The image of the connected numerals is normalized, preprocessed and then thinned before feature points are detected. Potential segmentation points are determined based on decision line that is estimated from the deepest/highest valley/hill in the image. The partitioning path is determined precisely and then the numerals are separated before restoration is applied. Experimental results on the NIST Database 19, CEDAR CD-ROM and our own collection of images show that our algorithm can get a successful recognition rate of 96%, which compares favorably with those reported in the literature. IS - 3 JF - Pattern Recognition EP - 634 TI - Segmentation of connected handwritten numeral strings VL - 36 SP - 625 KW - handwritten KW - segmentation KW - strings AU - Elnagar, Ashraf AU - Alhajj, Reda PY - 2003/03// UR - http://dx.doi.org/10.1016/S0031-3203(02)00097-3 ER -