Image Processing

Abstract
This paper is about a selected few image processing applications. Optical Character Recognition is the translation of images of handwritten, typewritten or printed text into machine-editable text. Then I have introduced the captcha that we so frequently encounter in common websites. An algorithm trying to solve or break a captcha has been explained.
Face detection is a growing and an important tool in security these days. It must be applied before face recognition. There are many methods for recognizing faces and a few of them are discussed in the paper.

Contents
Topic Pg No
Image Processing
Optical character recognition
Captcha
Braking Captcha
Face Detection
Algorithm for Face Detection
References

Image processing
Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

Typical Operations
Among many other image processing operations are:
 Geometric transformation such as enlargement, reduction, and rotation
 Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space
 Digital compositing or optical compositing (combination of two or more images).
 Interpolation, demosaicing, and recovery of a full image from a raw image format.
 Image editing (e.g., to increase the quality of a digital image)
 Image differencing (to determine changes between images)
 Image registration (alignment of two or more images)
 Image stabilization
 Image segmentation(partitioning a digital image into multiple regions)
 Extending dynamic range by combining differently exposed images
 2-D object recognition with affine invariance

References: [1] R. Feraud, O. Bernier, J.-E. Viallet, M. Collobert, D. Collobert, A Conditional Mixture of Neural Networks for Face Detection, Applied to Locating and Tracking an Individual Speaker, CAIL '97, Kiel, Germany, pp. 464-471, 1997. [2] T. S. Jebara, A. Pentland, Parametrized Structure from Motion for 3D Adaptive Feedback Tracking of Faces, IEEE CVPR Proceedings, pp. 144-150, 1997. [3] E. Osuna, R. Freund, F. Girosi, Training Support Vector Machines: an Application to Face Detection, IEEE CVPR Proceedings, pp. 130-136, 1997. [4] Suen, C.Y., et al (1987-05-29), Future Challenges in Handwriting and Computer Applications, 3rd International Symposium on Handwriting and Computer Applications, Montreal, May 29, 1987. Retrieved on 3 October 2008 [5] Tappert, Charles C., et al (1990-08), The State of the Art in On-line Handwriting Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 12 No 8, August 1990, pp 787-ff. Retrieved on 3 October 2008