Text Detection and Recognition Using Camera Based Images

The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones, wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images making them much more versatile than desktop scanners. Should gain solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Images captured from images can suffer from low resolution, perspective distortion, and blur, as well as a complex layout and interaction of the content and background.In this paper, we propose an efficient text detection method based on Maximally Stable Exterme Region (MSER) detector, saying that how to detect regions containing text in an image. It is a common task performed on unstructured scenes, for example when capturing video from a moving vehicle for the purpose of alerting a driver about a road sign . Segmenting out the text from a clutterd scene greatly helps with additional tasks such as optical charater recognition (OCR). The efficiency of any service or product, especially those related to medical field depends upon its applicability. The applicability for any service or products can b achieved by applying thr basic principles of Software Engineering.

[1]  Lewis D. Griffin,et al.  Multiscale Histogram of Oriented Gradient Descriptors for Robust Character Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[2]  C. V. Jawahar,et al.  Top-down and bottom-up cues for scene text recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Beom-Joon Cho,et al.  Locating characters in scene images using frequency features , 2002, Object recognition supported by user interaction for service robots.

[4]  Kai Wang,et al.  End-to-end scene text recognition , 2011, 2011 International Conference on Computer Vision.

[5]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Fumitaka Kimura,et al.  Recognition of Handwritten Kannada Numerals , 2006, 9th International Conference on Information Technology (ICIT'06).

[7]  Jerod J. Weinman Typographical Features for Scene Text Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[8]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Manik Varma,et al.  Character Recognition in Natural Images , 2009, VISAPP.

[10]  Andreas Dengel,et al.  ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images , 2011, 2011 International Conference on Document Analysis and Recognition.

[11]  Palaiahnakote Shivakumara,et al.  An Efficient Edge Based Technique for Text Detection in Video Frames , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[12]  Satoshi Ito,et al.  Co-occurrence Histograms of Oriented Gradients for Human Detection , 2010 .