A Novel Approach to Segmenting Ottaksharas from Handwritten Kannada Words

Transcript

1. A Novel Approach To
   Segmenting Ottaksharas from
   Handwritten Kannada Words
   Subhrajyoti Sen
   Smallcase
   

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2. Contents
   ▪ What is Optical Character Recognition (OCR)
   ▪ Steps in OCR
   ▪ Challenges faced
   ▪ Proposed Approaches
   ▪ Performance
   ▪ Future Scope
   

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3. Optical Character Recognition
   ▪ Optical character recognition (also
   known as OCR) is the conversion of
   images of handwritten or printed text into
   a machine-encoded text.
   ▪ The usage can vary from reading a
   business card to automated address tagging
   on the postal packages to reading number
   plates. The possibilities are huge.
   

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4. Steps in OCR
   1. Segmentation
   a) Line Segmentation
   b) Word Segmentation
   c) Character Segmentation
   2. Augmentation
   3. Classification
   

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5. Character Segmentation
   Input word image
   Segmented characters
   Process of segmenting a word into its constituting characters (including
   ottaksharas)
   

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6. Challenges
   ▪ Concept of subscript (ottakshara) as compared to languages
   written in the Latin script
   ▪ Ottaksharas are disconnected from the parent character and
   thus can be easily ignored by segmentation algorithms,
   thereby causing loss of data
   ▪ Ottaksharas are located diagonally below the parent character
   

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7. Proposed Approaches
   1. Iterative Approach
   2. Bounding Boxes Approach
   Implemented using OpenCV for Python
   

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8. Common Implementation
   • Convert image to grayscale. This is used to reduce
   image data.
   • Calculate binary threshold using Otsu optimization.
   Prevent manual calculation using trial and error.
   • Perform binary thresholding using the threshold.
   This reduces the image to a binary matrix.
   

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9. Iterative Approach
   1. Extract contours using RETR_EXTERNAL and CHAIN_APPROX_NONE
   flag.
   2. RETR_EXTERNAL returns only the outermost contours and thus ignores
   the minor inner contours that arise due to uneven ink or poor scanning.
   3. CHAIN_APPROX_NONE returns a list of co-ordinates along the contours.
   4. Sort the list of contours in descending order of area.
   5. Select the contour with the largest area.
   6. Iterate over the co-ordinates to find the one with the lower y value.
   7. Crop the part of the image below this point.
   8. The cropped image contains the ottaksharas.
   

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10. Iterative Approach
    

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11. Bounding Boxes Approach
    1. Sort contours from left-to-right.
    2. Draw bounding boxes are each contour.
    3. Ignore boxes with area less than 5% of the image area.
    4. Extract corner co-ordinates of remaining boxes.
    5. The bounding boxes whose top co-ordinate below half the height
    of the image contain an ottaksharas.
    6. Since the image is parsed left-to-right, the ottaksharas can be
    mapped to the previous parsed character.
    

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12. Bounding Boxes Approach
    O-01-00
    

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13. Segmentation Of Multiple Ottaksharas
    Both the approaches are able to segment multiple ottaksharas from
    words.
    The bounding boxes approach inherently segment multiple ottaksharas.
    The ottaksharas are numbered accordingly
    

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14. Segmentation Of Multiple Ottaksharas
    The iterative can be used to get the contours of the individual ottaksharas
    from the cropped image. Using the co-ordinates from the contours, they
    can drawn on a blank canvas, thus giving us images of single ottaksharas.
    

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15. Performance
    For a word of resolution 300x300 with a single ottakshara, the time
    taken by the scripts (in seconds) are
    Iterative Approach Bounding Boxes Approach
    0.0515 0.0081
    This is expected as the time complexity of the Iterative Approach is O(n2)
    while that of the Bounding Boxes Approach is O(n).
    

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16. Future Scope
    • Segmentation of ottaksharas connected to words
    • Segmentation of connected ottaksharas
    

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17. Thank You
    

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