TY - CHAP
T1 - A Turkish handprint character recognition system
AU - Çapar, Abdulkerim
AU - Taşdemir, Kadim
AU - Kilic, Özlem
AU - Gökmen, Muhittin
PY - 2003
Y1 - 2003
N2 - This paper presents a study for recognizing isolated Turkish handwritten uppercase letters. In the study, first of all, a Turkish Handprint Character Database has been created from the students in Istanbul Technical University (ITU). There are about 20000 uppercase and 7000 digit samples in this database. Several feature extraction and classification techniques are realized and combined to find the best recognition system for Turkish characters. Features, obtained from Karhunen-Loéve Transform, Zernike Moments, Angular Radial Transform and Geometric Features, are classified with Artificial Neural Networks, K-Nearest Neighbor, Nearest Mean, Bayes, Parzen and Size Dependent Negative Log-Likelihood methods. Geometric moments, which are suitable for Turkish characters, are formed. KLT features are fused with other features since KLT gives the best recognition rate but has no information about the shape of the character where other methods have. The fused features of KLT and ART classified by SDNLL gives the best result for Turkish characters in the experiments.
AB - This paper presents a study for recognizing isolated Turkish handwritten uppercase letters. In the study, first of all, a Turkish Handprint Character Database has been created from the students in Istanbul Technical University (ITU). There are about 20000 uppercase and 7000 digit samples in this database. Several feature extraction and classification techniques are realized and combined to find the best recognition system for Turkish characters. Features, obtained from Karhunen-Loéve Transform, Zernike Moments, Angular Radial Transform and Geometric Features, are classified with Artificial Neural Networks, K-Nearest Neighbor, Nearest Mean, Bayes, Parzen and Size Dependent Negative Log-Likelihood methods. Geometric moments, which are suitable for Turkish characters, are formed. KLT features are fused with other features since KLT gives the best recognition rate but has no information about the shape of the character where other methods have. The fused features of KLT and ART classified by SDNLL gives the best result for Turkish characters in the experiments.
UR - http://www.scopus.com/inward/record.url?scp=0142184005&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-39737-3_56
DO - 10.1007/978-3-540-39737-3_56
M3 - Chapter
AN - SCOPUS:0142184005
SN - 3540204091
SN - 9783540397373
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 447
EP - 456
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Yazici, Adnan
A2 - Sener, Cevat
PB - Springer Verlag
ER -