TY - GEN
T1 - Handwritten Arabic numerals recognition using multi-span features & Support Vector Machines
AU - Mahmoud, Sabri A.
AU - Olatunji, Sunday O.
PY - 2010
Y1 - 2010
N2 - In this work, a technique for handwritten Arabic (Indian) numerals recognition using multi-span features is presented. Angle, ring, horizontal, and vertical span features are used A II combinations of these features are tested and the combinations that result in the best recognition rates using Support Vector Machine (SVM) are identified The SVM classifier is trained with 15840 digits and tested with the remaining 5280 digits. It is shown that the recognition rates using angle & horizontal span features achieved better recognition rates than all other combinations including using all features. The recognition rates of SVM are compared with published results using Hidden Markov Model (HMM) and the Nearest Mean (NM) classifiers. The achieved average recognition rates are 99.4%, 97.99% and 94.35% using SVM, HMM and NM classifiers, respectively. The use of SVM and angle & horizontal span features give the highest recognition rates and are superior to HMM and NM classifiers for all digits.
AB - In this work, a technique for handwritten Arabic (Indian) numerals recognition using multi-span features is presented. Angle, ring, horizontal, and vertical span features are used A II combinations of these features are tested and the combinations that result in the best recognition rates using Support Vector Machine (SVM) are identified The SVM classifier is trained with 15840 digits and tested with the remaining 5280 digits. It is shown that the recognition rates using angle & horizontal span features achieved better recognition rates than all other combinations including using all features. The recognition rates of SVM are compared with published results using Hidden Markov Model (HMM) and the Nearest Mean (NM) classifiers. The achieved average recognition rates are 99.4%, 97.99% and 94.35% using SVM, HMM and NM classifiers, respectively. The use of SVM and angle & horizontal span features give the highest recognition rates and are superior to HMM and NM classifiers for all digits.
UR - https://www.scopus.com/pages/publications/78650265321
U2 - 10.1109/ISSPA.2010.5605423
DO - 10.1109/ISSPA.2010.5605423
M3 - Conference contribution
AN - SCOPUS:78650265321
SN - 9781424471676
T3 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
SP - 618
EP - 621
BT - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
ER -