Isolated handwriting recognition via multi-stage Support Vector Machines

Since isolated letter handwriting recognition is an essential step for online hand writing recognition, we present in this paper an efficient and writer independent isolated letter handwriting recognition system using pen trajectory modeling for feature extraction and a multi-stage Support Vector Machines (SVM) for classification. Inheriting the good discriminating ability of SVM while modeling sequential data, this hierarchical approach shows using 4 fold validation an average accuracy of 91.8% on the UJIpenchars database that consists of a collection of 1144 isolated letters written by 11 different writers. To the best of our knowledge, the best recognition rate achieved on this database is 89.15% using Dynamic Time Wrapping and 3 nearest neighbor classifier.