Segmentation and recognition of characters on Tulu palm leaf manuscripts

Abstract

This paper proposes an efficient method for segmentation and recognition of handwritten characters from Tulu palm leaf manuscript images. The proposed method uses an automated tool with a combination of thresholding and edge detection technique to binarise the image. Further projection profile with connected component analysis is used to line and character segmentation. Deep convolution neural network (DCNN) model used here to extract features and recognise segmented Tulu characters efficiently with a recognition rate of 79.92%. The results are verified using benchmark dataset, the AMADI_LontarSet to generalise our model to handwritten character recognition task. The results showed that our method outperforms from the existing state of art models.