Intrinsic Feature Extraction for Classification of Electrophotographic Printers

Techniques of banding analysis are particularly useful because print mechanisms can be examined from the rotating components, such as gears. The assessment of printer models can be developed by examining banding analysis because banding acts as an intrinsic signature. Various patterns of grayscale compositions are generated and printed with the test printers. FFT was used to analyze the signal from the printed and scanned images. The FFT based algorithm written in MATLAB determined the principal banding frequency. Once principal banding frequency was determined, proper classification of printer model was identified. Implementation of Optical Character Recognition (OCR) algorithms were experimented in designating specific character profiles for a sample text document, which was used for extracting intrinsic features. From sample text image, a projection based algorithm was constructed to segment out each character so that each individual letter could be compared and matched for proper classification. The OCR algorithm will be a part of the printer classification from text-only document. Introduction Forensics in electrophotographic (EP) printing has primary objectives of investigating counterfeiting, forgeries, and homeland security matters. For example, fake licenses, passports, money and other legal documents are printed using EP and ink-jet techniques. Many law enforcement agencies, cyber-security, and forensic police need to be able to trace these important documents, from their origins, in order to prevent these fraudulent crimes. The printers and models used for these crimes need to be determined to find out the exact origination of the documents. The drums and mirrors inside the printer as well as banding pattern factors including timing, intensity and width of pulses of the laser light provide valuable evidence in assigning these attributes to printer models. Image processing can be used to extract intrinsic features from an image. It can lead to pattern recognition which includes three common phases referred to as image segmentation, feature extraction and classification [1]. Image segmentation involves process of finding a particular segment and isolating it from the rest of the image. This newly cropped object can be measured so that significant characteristics of the object is determined and is put into a feature vector, a vector that contains the set of produced features. After determining the qualities of the object, it is ready to be classified and the object has been assigned to several pre-established classes that represent all possible types of objects that are expected to exist in image. One of the primary methods of analyzing a single character includes placing it in a rectangular cell. The character sits on an imaginary line, known as the baseline, and can be used in defining height. The ascent distance of the character is known as the distance from the baseline to the top of the cell. Ascender characters include A, k, l, r, and W. There are also characters that extend below the baseline, such as g, j, q, p, and these are known as descender characters [2]. As shown in Figure 1, a character can be analyzed by these key terms. The distance from the baseline to the bottom of the cell is called the descent distance. The sum of the ascent and descent distance is defined as the height of the character and is typically expressed in points. 1 inch is equivalent to 72 points. Figure 1: Individual Character and definitions. Optical Character Recognition (OCR) is a method of using software to recognize character images as typewritten text. The recognition of typewritten versions of different text scripts makes it possible for classifying the text using parameters such as character height, width, horizontal and vertical projection, to differentiate amongst other individual characters. descent ascent g Height = ascent + descent