Structural offline handwriting character recognition using levenshtein distance

Research in offline handwriting recognition for unconstrained text remains a difficult challenge. Some problems such as noise in image, skew of text, cursive letters, and various handwriting style is still an open problem. Many method has been researched to solve those problems, such as k-NN, Neural Network, SVM and HMM. And to improve the recognition result, there are many methods can be implemented in the prerocessing stage. One of them is Kimura's method for slant correction that using chain code for character slant prediction. Those method consumes time and resource in its computation, meanwhile the accuracy is not improved significantly. Therefore, this paper propose to create handwritten character into graph with string representation based on structural approach. The purpose is to provide ability in improving recognition accuracy without relying on normalisation technique. The similarity distance between graphs measured using levenshtein distance. Experiment conducted to recognize handwritten upper-case letters and digits character images which taken from ETL-1 AIST databases. Levenshtein distance has an accuracy of 84.69% on digits and 67.01% on alphabet with 5% size of data for training and value 10 for string representation length. As a comparison, the Kimura's method are implemented for slant correction which results in a reduction of accuracy until 6%. Comparisons also made with some of previous work.