ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONAL RANDOM FIELD MODEL AND OCR

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Pratik Yadav
Prabhudev Irabashetti

Abstract

In Natural Scene Image, Text detection is important tasks which are used for many content based image analysis. A maximally stable external region based method is used for scene detection .This MSER based method includes stages character candidate extraction, text candidate construction, text candidate elimination & text candidate classification. Main limitations of this method are how to detect highly blurred text in low resolution natural scene images. The current technology not focuses on any text extraction method. In proposed system a Conditional Random field (CRF) model is used to assign candidate component as one of the two classes (text& Non Text) by Considering both unary component properties and binary contextual component relationship. For this purpose we are using connected component analysis method. The proposed system also performs a text extraction using OCR.

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How to Cite
Pratik Yadav, & Prabhudev Irabashetti. (2021). ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONAL RANDOM FIELD MODEL AND OCR. International Journal of Innovations in Engineering Research and Technology, 1-5. https://repo.ijiert.org/index.php/ijiert/article/view/611
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How to Cite

Pratik Yadav, & Prabhudev Irabashetti. (2021). ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONAL RANDOM FIELD MODEL AND OCR. International Journal of Innovations in Engineering Research and Technology, 1-5. https://repo.ijiert.org/index.php/ijiert/article/view/611