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.
Optical character recognition
Algorithm for Face Detection
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.
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 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...
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