Asian Transactions on Computers

Volume: 01, Issue: 05, November 2011
ISSN 2221-4275


Title: Representing Programming Object Metadata Using the IEEE LOM Methodology
Authors: Daniyal M. Alghazzawi
Paper ID: ATC-70110058
Pages: 1-14
Abstract: Software engineering is concerned with developing reusable high-quality programming objects such as architectures, source code, data, designs, documentation, templates, human interfaces, plans, requirements, and test cases. In order to deploy such as these objects, we need to maintain a metadata for them. In this paper, we used the term Programming Object Metadata for such a metadata. Currently, there is no standard for this type of metadata. Therefore, this paper focuses on developing a metadata for the programming objects by using IEEE Learning Object Metadata 1484.12.1-2002 methodology. The proposed metadata was validated by providing an example of a Programming Object Metadata (POM) in XML format.

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Title: Handwritten Character Segmentation System For Bangla Text Containing Modifier And Overlapped Characters
Authors: Md. Mojahidul Islam, Md. Aktaruzzamn, Md. Farukuzzaman, Md. Shohidul Islam
Paper ID: ATC-30126054
Pages: 15-19
Abstract: This paper deals with the design and development of a Bangla offline Handwritten Character Segmentation system. Special focus of this work was on skewed text containing modifier and overlapped characters. The text pages were scanned using flatbed scanner and saved as 256 gray-level image in bmp file format. This image file was used as the input to the developed system. In order to segment the skewed lines, the text was divided into a number of vertical stripes called frame. Each frame was segmented by horizontal pixel scanning method and concatenated in proper order to form the original text line. Based on vertical pixel scanning method lines were segmented into words. In character segmentation, isolated characters were segmented by vertical scanning method but connected and overlapped characters are segmented using degenerated lower chain. The system was tested for handwritten text pages of five individual writers. The average segmentation accuracy rate of the system was about 94%.

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