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Thai-OCR

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    Thai-OCR Thai-OCR Presentation Transcript

    • The Development of Thai-OCR by Using Dynamic Time Warping technique with Font Files
    • Members
      • Mr. Prayook Jatesiktat M . 6/3 No. 15
      • Mr. Teerapon Thewmorakot M . 6/1 No. 17
      • Mr. Pongsate Tangseng M . 6/1 No. 19
      Adviser Aj. Ptoomsiri Songsiri
      • Old transformation
      • Waste time resource
      • Waste personal resource
      • If you use scanning, waste memory and difficult to edit
      • OCR transformation
      • Easier and faster
      • Easy to edit
      • Require a little memory
      Introduction
    • Introduction Working diagram of Artificial Neural Network Feature extraction Learning until get satisfying value Target data Input data Adjust weight of feature value Get pattern for analyze real input data (need skill and experience of researchers) (need the large number of data) (waste much time) (limited fonts are allowed)
    • Objective
      • To develop Thai-OCR algorithms using font files to solve the problems in OCR research.
    • Concept Font files Printed matters Same pattern Easy to recognize
    • Range of research
      • Characteristic of input data
        • Thai language only
        • Grayscale or monochrome bitmap images
        • No pictures or tables
        • Clear alphabets
        • Create test data by printing on A4 paper and scanning with resolution at 300 dpi
      • Characteristic of font files
        • Normal fonts
        • Users must know font’s name and have font file
      • Learning method
        • Study in pre-processing and processing only
        • Comparison efficiency between Hausdorff Distance and Dynamic Time Warping technique
      Range of research
    • Working structure Post Processing Preprocessing Processing Hausdorff Distance Dynamic Time Warping Efficiencies comparison
    • Result Table 1 : Time usage 26.45 29.89 Average 27.67 33.33 Cordia New 30.00 30.67 PS Pimpdeed 21.67 25.67 Angsana New Hausdorff Distance Dynamic Time Warping Time usage (second/1 page) Fonts
    • Result Table 2 : Accuracy 76.19 76.50 Average 75.71 77.07 Cordia New 82.19 71.80 PS Pimpdeed 70.68 80.64 Angsana New Hausdorff Distance Dynamic Time Warping Accuracy (%) Fonts
    • Conclusion
        • In time usage comparison, Hausdroff Distance use less time than Dynamic Time Warping.
        • In accuracy comparison, accuracy efficiencies are up to type of font
    • Development
      • It will be better if we can read data form font files.
      • It will be useful if we can recognize type of font without input from user.
      • It can be applied to other languages.
    • References
      • http://www.ce.kmitl.ac.th/project/display1.php?id1=483
      • http://202.28.94.55/web/320491/2548/web1/g15/index.html
      • http://mad.cpe.ku.ac.th/~pp/rm/papers/Paper_10.pdf
      • Keogh, E.J. and Pazzani, M.J. 2001. Derivative Dynamic Time Warping. (Online). Available : http://www.cs.ucr.edu/~eamonn/sdm01.pdf [2007 March, 22]
      • Gonzalez, R.C., Woods, R.E. (2002). Digital Image Processing (Second Edition). New Jersey :Printice-Hall.
      • Premnath Dubey. 2006. Optical Character Recognition An Overview. (Online). Available: http://www.tcllab.org/events/uploads/Anlp_Presentation.pdf [2007 March, 15]