Offline cursive handwriting segmentation - Fabian, Melanie E.; Sayo, Archibald P. (2004)
A handwriting segmentation technique that separates cursive words into its character components was developed in this project. The segmentation technique starts with the preprocessing of handwritten words. Local features were then extracted from the word images. These features were used in training the neural network to generate possible segmentation points. The training was done in three different methods: using contour feature alone, using histogram feature alone and using both features. These methods of training were implemented in five different database sizes: 100, 1841, 5698, 6013, and 11711 word images. The word images were written by multiple authors and the number of characters ranges from 2 to 12. The characters may or may not be connected. The output of the neural network is the probabilities that a frame or column of an image is a segmentation point. This output is then decoded to generate the segmentation points for the images. An error correction algorithm using the hole feature of characters such as: a b d e f h l o p q was used to increase the over-all accuracy of the trained network. The handwriting segmentation technique was able to achieve the best train accuracy of 88.34% and best cross validation accuracy of 87.70%. These accuracies were obtained using the database with 100 word images. The best average testing accuracy of 51.67% and best total testing accuracy of 49.97% were obtained using the histogram-based segmentation implemented in the database with 5698 word images.
Thesis (B.S., EEE)--University of the Philippines Diliman.
Keywords: Writing -- Data processing; Optical character recognition; Neural network (Computer science); Handwriting segmentation technique; Cursive handwriting