A Novel Text Localization Scheme for Camera Captured Document Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

In this paper, a hybrid model for detecting text regions from scene images as well as document image is presented. At first, background is suppressed to isolate foreground regions. Then, morphological operations are applied on isolated foreground regions to ensure appropriate region boundary of such objects. Statistical features are extracted from these objects to classify them as text or non-text using a multi-layer perceptron. Classified text components are localized, and non-text ones are ignored. Experimenting on a data set of 227 camera captured images, it is found that the object isolation accuracy is 0.8638 and text non-text classification accuracy is 0.9648. It may be stated that for images with near homogenous background, the present method yields reasonably satisfactory accuracy for practical applications.

Keywords

Text detection Feature map Background suppression Textness features Text non-text classification MLP 

Notes

Acknowledgements

The authors are thankful to the Department of Computer Science and Engineering of Aliah University for providing every support for carrying out this work. The first author is also thankful to Aliah University for providing research fellowship.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAliah UniversityKolkataIndia

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