Image Scene Segmentation and Recognition on Texture Base Approach

Image Scene Segmentation and Recognition on Texture Base Approach


The area of scene text recognition focuses on the problem of recognizing arbitrary text in images of natural scenes. Examples of scene text include street signs, business signs, grocery item labels, and license plates. Text detection in images is an important step to achieve image content retrieval. In this work, an efficient algorithm which can automatically detect, localize, segmentation and extract horizontally aligned text in images with complex backgrounds is presented. The proposed approach is based on the application of a method for edge detection, and the localization of text regions using projection profile analyses and geometrical properties. With the increased use of smart phones and digital cameras, the ability to accurately recognize text in images is becoming increasingly useful and many people will benefit from advances in this area. The goal of this work is to develop methods for improving scene text recognition. We do this by incorporating new types of information into models and by exploring how to compose simple components into highly effective systems. We focus on three areas of scene text recognition, each with a decreasing number of prior assumptions. First, we introduce techniques for character recognition, where word and character bounding boxes are assumed. The output of the algorithm are text boxes with a simplified background, ready to be fed into an Image Text Detection and Recognition (ITDAR) algorithms for subsequent character recognition. Our proposal is robust with respect to different font sizes, font colors and background complexities.


Product Description

ISBN 13: 978-93-84698-18-8
ISBN 10: 93-84698-18-8
Book language: English
By (Author): Mr. Bharatratna Pralhadrao Gaikwad and Dr. Ramesh R. Manza
No. of Pages: 102
Published on: 10-08-2016
Category: Computer Science and Information Technology

Additional information



Author's Name

Dr. Ekbal Rashid


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