Study on feature extraction methods for character recognition of Balinese script on palm leaf manuscript images

Abstract

The complexity of Balinese script and the poor quality of palm leaf manuscripts provide a new challenge for testing and evaluation of robustness of feature extraction methods for character recognition. With the aim of finding the combination of feature extraction methods for character recognition of Balinese script, we present, in this paper, our experimental study on feature extraction methods for character recognition on palm leaf manuscripts. We investigated and evaluated the performance of 10 feature extraction methods and we proposed the proper and robust combination of feature extraction methods to increase the recognition rate.

Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

“…In our previous work [28], we investigated and evaluated the performance of 10 feature extraction methods with two classifiers, k-NN (k-Nearest Neighbor) and SVM (Support Vector Machine), in 29 different schemes for Balinese script on palm leaf manuscripts. After evaluating the performance of those individual feature extraction methods, we found that the Histogram of Gradient (HoG) features as directional gradient-based features [9,49] (Figure 5), the Neighborhood Pixels Weights (NPW) [50] (Figure 6), the Kirsch Directional Edges [50], and Zoning [12,32,50,51] (Figure 7) give very promising results.…”

“…We then proposed a new feature extraction method applying NPW on Kirsch edge images ( Figure 8) and concatenated the NPW-Kirsch with two other features, HoG and Zoning method, with k-NN as the classifier. Type of Zoning (from left to right: vertical, horizontal, block, diagonal, circular, and radial zoning) [28]. Type of Zoning (from left to right: vertical, horizontal, block, diagonal, circular, and radial zoning) [28].…”

“…The Southeast Asian manuscripts with different scripts and languages provide real challenges for document analysis methods, not only because of the different forms of characters in the script, but also because the writing style of each script (e.g., how to join or separate a character in a text line) differs. It ranges widely from a binarization process [23][24][25], text line segmentation [26,27], and character and text recognition tasks [25,28,29], to the word spotting methods [30].…”

“…In that first work, an experimental study on feature extraction methods for character recognition of Balinese script was performed [28]. For the second work, a training-based method with neural network and unsupervised feature learning was used to increase the recognition rate [29].…”

“…Previous studies on isolated character recognition in palm leaf manuscripts have already been reported, but only with the Balinese script as the benchmark dataset [28,29]. In that first work, an experimental study on feature extraction methods for character recognition of Balinese script was performed [28].…”