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

Authors

  • Pratik Yadav ME II Computer , Department of Computer Engineering., Pune University / Vishvabahrti College Of Engineering, Ahmednagar,India [email protected]
  • Prabhudev Irabashetti Assistant Professor, Department of Computer Engineering, Pune University / Vishvabahrti College Of Engineering, Ahmednagar, India [email protected]

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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Published

2021-03-27

Issue

2015: ICITDCEME

Section

Articles

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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