Line Segmentation of Javanese Image of Manuscripts in Javanese Scripts

Research Area: Volume 2 Issue 3, May. 2013 Year: 2013
Type of Publication: Article Keywords: Connected Component, Javanese Manuscript Segmentation, Moving Average, Projection Profile
  • Anastasia Rita Widiarti
  • Agus Harjoko
  • Marsono
  • Sri Hartati
Journal: IJEIR Volume: 2
Number: 3 Pages: 239-244
Month: May
Segmentation is an important stage in automatic transliteration process of a manuscript image. One of the segmentation approaches generally used to get an image of the scripts of a scrip image is performing line segmentation and then performing segmentation of script images on the result of the line segmentation. Line segmentation of image of manuscripts in Javanese scripts is often difficult because there are lines of images which shouldn’t be on the same line, but are in one line area of the image, and there are even images in different but overlapping lines. This paper offers an idea to use moving average to smooth the curve of vertical projection result of image of manuscripts in Javanese scripts as an initial guide of line separation. Next, to separate parts of scripts in different lines but in the same line or in overlapping lines, average height data and standard deviation of average height of every objects are use to get information on estimation of the height of line image. Image connectivity concept in an object is also used to separate script images in different but overlapping lines. From the test result of 4 images of manuscripts in Javanese scripts from different writers with different writing styles, average percentage of correctness of line segmentation obtained was 93.19% with standard deviation of 4.55%. Mistakes of line segmentation were mostly caused by sandangan of a script in the previous line which joined the next line, and cutting imperfect scripts. However, from the percentage of correctness of line segmentation, it could be concluded that the combination of various image processing techniques used in line segmentation was relatively good.

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