Line Detection Model and Adaptive Threshold Based Image Segmentation For Handwriting Word Recognition

Handwriting word recognition has been researched many researchers. The most method used is Line based representation. However, it has a weakness, which is high cost to recognize object. In this research, line detection model is proposed to determine the right, left, top and bottom object boundary. In order to separate all of objects, the line detection has been conducted for segmentation iteratively. All of objects are labeled to get number of object in image. One of parameters that have effect to the segmentation results is threshold value. Errors in the determination of the threshold will affect to the segmentation results. However, it is necessary to determine the adaptive threshold value. In this research, Otsu’s method is proposed to achieve the best threshold value. The threshold value depends on the testing image. 84 images have been used as training set. Proposed method has been tested by using 30 images. In this case, 20 images have single line handwriting word and the 10 images have two or more lines handwriting word. The experimental results show that recognition rate average is 82.779%, single line handwriting word has higher recognition rate than multi line handwriting word.