TY - GEN
T1 - Identifying handwritten text in mixed documents
AU - Farooq, Faisal
AU - Sridharan, Karthik
AU - Govindaraju, Venu
PY - 2006
Y1 - 2006
N2 - In this paper we present a system for classification of machine printed and handwritten text in mixed documents. The classification is performed at the word level. We propose a feature extraction algorithm for each word image based on Gabor filters followed by classification using an Expectation Maximization(EM) based probabilistic neural network that reduces overfitting of training data. An overall precision of 94.62% was obtained for the Arabic script using the modified neural network. The accuracies obtained using a simple backpropagation neural network and an SVM were 83.33% and 90.26% respectively.
AB - In this paper we present a system for classification of machine printed and handwritten text in mixed documents. The classification is performed at the word level. We propose a feature extraction algorithm for each word image based on Gabor filters followed by classification using an Expectation Maximization(EM) based probabilistic neural network that reduces overfitting of training data. An overall precision of 94.62% was obtained for the Arabic script using the modified neural network. The accuracies obtained using a simple backpropagation neural network and an SVM were 83.33% and 90.26% respectively.
UR - https://www.scopus.com/pages/publications/34047223253
U2 - 10.1109/ICPR.2006.676
DO - 10.1109/ICPR.2006.676
M3 - Conference contribution
AN - SCOPUS:34047223253
SN - 0769525210
SN - 9780769525211
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1142
EP - 1145
BT - Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
T2 - 18th International Conference on Pattern Recognition, ICPR 2006
Y2 - 20 August 2006 through 24 August 2006
ER -