Image Processing

Topics: Optical character recognition, Computer vision, Image scanner Pages: 5 (1404 words) Published: November 13, 2008
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
TopicPg 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

Optical character recognition
Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques. Optical character recognition (using optical techniques such as mirrors and lenses) and digital character recognition (using scanners and computer algorithms) were originally considered separate fields. Because very few applications survive that use true optical techniques, the OCR term has now been broadened to include digital image processing as well. Early systems required training (the provision of known samples of each character) to read a specific font. "Intelligent" systems with a high degree of recognition accuracy for most fonts are now common. Some systems are even capable of reproducing formatted output that closely approximates the original scanned page including images, columns and other non-textual components. The accurate recognition of Latin-script, typewritten text is now considered largely a solved problem. Typical accuracy rates exceed 99%, although certain applications demanding even higher accuracy require human review for errors. Other areas--including recognition of hand printing, cursive handwriting, and printed text in other scripts (especially those with a very large number of characters)--are still the subject of active research. Note:

Accuracy rates can be measured in several ways, and how they are measured can greatly affect the reported accuracy rate. For example, without the use of word context (basically a dictionary of words) to correct "spelling" errors, an error rate of 1% (or 99% accuracy) measured letter-by-letter may...

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
Continue Reading

Please join StudyMode to read the full document

You May Also Find These Documents Helpful

  • DIGITAL IMAGE PROCESSING Essay
  • Secure Atm by Image Processing Essay
  • Licence Plate Recognition from Still Images and Videos Essay
  • Essay on Digital Image Processing
  • Image Processing Essay
  • Image Processing Essay
  • Image Processing Algorithms Implementation on Fpga Essay
  • Essay on Digital Image Processing Techniques

Become a StudyMode Member

Sign Up - It's Free