Rapeeporn Chamchong

Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu’s algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character’s image. The results from this research may be applied to optical character recognition (OCR) in the future.

Content-Based Text Classifiers for Pornographic Web Filtering

Due to the flood of pornographic web sites on the internet, effective Web filtering systems are essential. Web filtering based on content has become one of the important techniques to handle and filter inappropriate information on the web. We examine two machine learning algorithms (support vector machines and Naive Bayes) for pornographic web filtering based on text content. We then focus initially on Thai-language and English-language web sites. In this paper, we aim to investigate whether machine learning algorithms are suitable for web sites classification. The empirical results show that the classifier based support vector machines are more effective for pornographic web filtering than Naive Bayes classifier after testing, especially an effectiveness for the over-blocking problem.

Improving vehicle detection by adapting parameters of HOG and kernel functions of SVM

Currently, vehicle detection suffers from low performance in terms of accuracy and time costs in real-time application. Histograms Oriented of Gradients(HOG) and Support Vector Machine(SVM) are popular methods used to address these problems, however, while they can give high accuracy, detection is still too slow for real-time application. The V-HOG method has previously been proposed to reduce detection time in real-time application by adjusting HOG structures. Although V-HOG detection is faster than that of HOG, the accuracy is lower. Therefore, this paper proposed to improve accuracy and classification time by adjusting HOG parameters and SVM kernel functions. The experimental results showed that the proposed method results in 100% accuracy and supports real-time application.

Data extraction in form documents with gray-level background using morphological processing

Automatic reading of form documents with gray-level background needs a preliminary task of preparing a clean image of the data to be recognized using OCR. In this paper, we present a data extraction process for such documents. First, the preprinted background is removed by decomposing the histogram of the input image. Reference lines are then subtracted from this image. Finally, the lost parts of character images overlaying with reference lines can be restored. The experiments carried out with a bank cheque will be given to illustrate the usefulness of such an algorithm.