Title :
A new segmentation algorithm for handwritten word recognition
Author :
Blumenstein, M. ; Verma, B.
Author_Institution :
Sch. of Inf. Technol., Griffith Univ., Brisbane, Qld., Australia
Abstract :
An algorithm for segmenting unconstrained printed and cursive words is proposed. The algorithm initially oversegments handwritten word images (for training and testing) using heuristics and feature detection. An artificial neural network (ANN) is then trained with global features extracted from segmentation points found in words designated for training. Segmentation points located in “test” word images are subsequently extracted and verified using the trained ANN. Two major sets of experiments were conducted, resulting in segmentation accuracies of 75.06% and 76.52%. The handwritten words used for experimentation were taken from the CEDAR CD-ROM. The results obtained for segmentation can easily be used for comparison with other researchers using the same benchmark database
Keywords :
feature extraction; handwritten character recognition; image segmentation; learning (artificial intelligence); neural nets; cursive words; feature extraction; handwritten word recognition; image segmentation; learning; neural network; Artificial neural networks; Australia; Character recognition; Gold; Handwriting recognition; Image segmentation; Information technology; Postal services; Telephony; Testing;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833544