Segmentation of Degraded Text using Dynamic Profile Projection in Handwritten Gurmukhi Script

Kaur, Karamjeet; Bathla, Ashok Kumar

doi:10.18535/ijecs/v5i11.99

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…When space before character is less than the threshold space then it is combined with the previous character which is shown in Eq. (8). When space after character is less than the threshold space then it is combined with the next character, which is shown in Eq.…”

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confidence: 99%

Identification of Broken Characters in Degraded Documents

Ramalingam¹,

Bhojan²

2018

IJIES

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Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing.

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Section: = −mentioning

confidence: 99%