Automatic assessment mark entry system using local binary pattern (LBP) and salient structural features
Offline handwritten digit 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 time...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/11689/1/articleDetails.jsp_arnumber%3D7072747 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7072747 http://eprints.utp.edu.my/11689/ |
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| Summary: | Offline handwritten digit 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 time consuming, not cost effective and prone to human mistakes. Thus, 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 training data contain three sets of LBP values 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 scaling the digits `0 to 9' from the highest to the lowest similarity score as compared with the sample using chi square distance. The recognition rate can be greatly improved to about 95% by verifying the ranking of chi square distance with the salient structural features of digits. |
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