AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits

Environmental impacts of paper handouts vs. Online handouts-from a life cycle assessment prospective

The purpose of this study was to address the concerns about sustainability between the uses of hard copy reference documents versus online copies. This project presents a Life Cycle Assessment (LCA) study comparing the production and use of a hard copy handout versus an online handout. For the purpose of this study, paper handouts and online handouts are two different and independent processes. This means that they not only have different outputs but also have different manufacturing stages in order to get to the final result. The scope of this study is “cradle to grave,” starting with the extraction of the raw materials, followed by paper and computer production processes, transportation, product use and disposal/recovery. Results indicate that under the made assumptions, the use of a paper handout has more environmental impacts. At the same time among the evaluated processes, paper production has higher environmental impacts

AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis