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
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.
Click “In This File”. Designate the desired pages and click OK. Acrobat will apply OCR to the scanned document. Note: the quality of the OCR can vary significantly depending on the quality of the scan.
Images of parts of the Beowulf manuscript are scanned in 24-bit colour, both under visible and ultraviolet light. These images may be of entire pages, or just single words or letters. The resulting image files are huge: at a maximum size of 2320 by 3072 pixels each image takes up about 20-25 MB, however they can reveal even more information than would physical examination of the original manuscripts, for example allowing the detection of alterations to the manuscripts and revealing letters that have been obscured by repairs to eighteenth century fire damage.
In this project, issues regarding the Hough Transform for line detection are considered. The first several sections deal with theory regarding the Hough transform, then the final section discusses an implementation of the Hough transform for line detection and gives resulted images. The program, images, and figures for this project are implemented using the Matlab.
Of the phonetic values that he assigned to hieroglyphs, five were correct (p, t, i, n, and f). (Budge 54) In 1814, he revealed the way in which the hieroglyphic signs were to be read by studying the direction in which the birds and other animals were all facing. He also was able to correctly identify some single-consonant hieroglyphic signs.... ...
82).” According to Walter Ong, the act of communication through writing heightens ones consciousness and begins to change the way in which the writer thinks. This in turn facilitates the development of increasingly sophisticated technological advancements. Early pictographs were typically monotone and very simplistic in nature. However, as the technology evolved, humankind developed multi-hued writing media that improved the visual accuracy of the images created and subsequently improved the complexity of the message delivered. Essentially more visual detail equals a more complex symbology and abstraction. Some major milestones in the evolution of communication technology include the simplification of earlier literal depictions in the late Paleolithic era, the development of the first “alphabets” as quasi-abstract symbols representing the basic sounds of spoken language. These early alphabets were extremely complex and cumbersome until the Phoenicians developed a “totally abstract and alphabetical system of twenty-two simple phonetic signs, replacing the formidable complexity of cuneiform and hieroglyphs (Higgins, 2003).” The inhabitants of Greece and Rome adopted this system of writing which was in effect by 1500 B.C. and later developed what we know as the
It was no longer enough for the 26 letters of the alphabet to function only as phonetic symbols. The industrial age transformed these signs into abstract visual forms projecting a variety of shapes to be consumed by the public eye.
This article examines the use of multiple authentication methods to increase the security of a system. Moreover, with the use of biometric methods, the author seeks to show that authentication can occur continually during the time the resources are being utilized.
Biometrics is described as the use of human physical features to verify identity and has been in use since the beginning of recorded history. Only recently, biometrics has been used in today’s high-tech society for the prevention of identity theft. In this paper, we will be understanding biometrics, exploring the history of biometrics, examples of today’s current technology and where biometrics are expected to go in the future.
Biometrics-based authentication applications include workstation, network, and domain access, single sign-on, application logon, data protection, remote access to resources, transaction security and Web security (Campbell, 1995). Utilized alone or integrated with other technologies such as smart cards, encryption keys and digital signatures, biometrics are set to pervade nearly all aspects of the economy and our daily lives (Campbell, 1995). Among the features measured are; face, fingerprints, hand geometry, iris, and voice (Campbell, 1995).
The Greek alphabet first appears in archeological records during the 8th century BCE. The Greek alphabet, however, was not the first writing system used to write Greek as the linear B script was used during Mycenaean times. The linear B script was lost in 1100 BCE, along with all knowledge of writing until the Greek alphabet was developed. The Greek alphabet was originally adopted from the Phoenician writing system to represent the Greek language. Even though the Greek alphabet is only used for the Greek language today, it is the root of many modern day scripts.
For a computer to run a program, it needs to translate the English words into something that the computer can understand. This is usually done through ASCII (American Standard Code for Information Interchange), which is a way for a computer to change every letter and symbol into the appropriate binary string. This translation is crucial for a computer to work with non-binary input. The brain also does symbol manipulation much faster than we realize. Pictures and words that are flashed very quickly to a person can still be picked up without much loss of information.
Mark interesting features of the text. A feature might be a phrase, word, part of a word (for example, a vowel or consonant whose sound strikes you as interesting), even a punctuation mark or line or stanza break. Readers who are proficient in scansion (the analysis of metrical patterns) also use marks to identify stressed and unstressed syllables and metrical feet.
Pattern recognition is when you look for similarities among and within small, decomposed problems that help solve complex problems more efficiently. An example of this would be drawing a dog, if we wanted to draw a dog we wouldn’t have to think too long because we know all dogs have 4 legs, eyes and a tail so knowing that it would make it easier and quicker to complete many different drawings. Finding patterns in problems makes problem solving a lot easier and it gives you a place to start when fixing a new problem. Pattern recognition is a process based on 5 key
Computer vision is a discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images in terms of the properties of the structures present in the scene. It combines the knowledge from computer science, electrical engineering, mathematics, physiology, biology and cognitive science in order to understand and simulate the operation of the human vision system. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract data from the images. As a technological discipline, computer vision seeks to apply its theories and model to the construction of computer vision systems.
“The term -information security- means protecting information and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction” (United States Code, 2008). In order to ensure the identity of who is trying to access the information, the concept of “Biometric Technology” has been developed in the last years. This essay will start explaining this concept and the characteristics of its development through the time. Then, the essay will offer a brief explanation of biometric systems operation and a description of different biometric systems developed until now. Finally, this research analyzes the current and future applications and the issues that surround it.