Automatic assessment mark entry system using local binary pattern (LBP) and salient structural features

Lim Lam Ghai, Suhaila Badarol Hisham, Norashikin Yahya

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 4th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2014
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479956869
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Control System, Computing and Engineering 2014 - Batu Ferringhi, Penang, Malaysia
Duration: 28 Nov 201430 Nov 2014
Conference number: 4th (Proceedings)


ConferenceIEEE International Conference on Control System, Computing and Engineering 2014
Abbreviated titleICCSCE 2014
CityBatu Ferringhi, Penang
Internet address


  • chi square distance
  • handwritten recognition
  • local binary pattern
  • structural feature

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