Text Detection Using Log Operator And Clustering Method using OpenCV

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Text Detection Using Log Operator And Clustering Method For Computer Vision Application

Platform : Python

Delivery Duration : 3-4 working Days

100 in stock

SKU: Text Detection For Computer Vision Application Categories: ,

Description

OBJECTIVE

This approach is based on the application of a colour reduction technique, a method for edge detection, and the localization of text regions using projection profile analyses and geometrical properties.

DEMO VIDEO

ABSTRACT

Text detection in images or videos is an important step to achieve multimedia content retrieval. In this paper, an efficient algorithm which can automatically detect, localize and extract horizontally aligned text in images with complex backgrounds is presented. The proposed approach is based on the application of a colour reduction technique, a method for edge detection, and the localization of text regions using projection profile analyses and geometrical properties. The text contains vital and useful information which is embedded in various types of documents and natural scene. The extraction of text from a natural image is a challenging task. The text is detected and is extracted in this way that it readable by another person without any difficulty. In this paper we proposed a fast text localization method in which sobel edge detection and clustering method is used to localize all the possible edges in the image.

EXISTING SYSTEM

  • Manual Segmentation
    •  This method is not fully automatic because it involves few parameters that need manual specification.
    • Image Threshold 

PROPOSED SYSTEM

  • Image pre-processing is the term for operations on images at the lowest level of abstraction.
  •  These operations do not increase image information content but they decrease it if entropy is an information measure.
  • The aim of pre-processing is an improvement of the image data that suppresses undesired distortions or enhances some image features relevant for further processing and analysis task.

BLOCK DIAGRAM

Text Detection for computer vision application

APPLICATIONS

  • Automatic sign recognition technique
  • Computer vision techniques

DRAW BACKS

  • Difficulties are there to find optimal gradient
  • Poor Edge detection.

SOFTWARE REQUIREMENTS

  • Python
  • Opencv
  • Numpy

RESULT

By this we can identify the text and different character set, use of this artificial intelligence can be upgraded.

REFERENCES

[1]  J. Almaz´an, A. Gordo, A. Forn´es, and E. Valveny. Word spotting and recognition with embedded attributes. In TPAMI, 2014.

[2] Katherine L Bouman, Golnaz Abdollahian, Mireille Boutin, and Edward J Delp. A low complexity sign detection and text localization method for mobile applications. TMM, 13(5):922–934, 2011.

[3] Leo Breiman. Random forests. Machine Learning, 2001.

[4]  Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011.

[5]  Chih-Chung Chang and Chih-Jen Lin. The attraction of visual attention to texts in real-world scenes. Journal of Vision, 2012.

[6]  Huizhong Chen, Sam S. Tsai, Georg Schroth, David M. Chen, Radek Grzeszczuk, and Bernd Girod. Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In ICIP, 2011.

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