Abstract: Image analysis and recognition includes reading text, by digitally scanning a surface, locating the printed material in that digital image, and then recognizing words, phrases, or numbers based on their two dimensional, low frequency Fourier harmonics. One objective is to specifically apply this method of recognition to the postal industry, to include all shipping and labeling applications. Once the image of a word is digitized and isolated, a two-dimensional Fourier transform is computed of the digital image. The process is accomplished in the same manner regardless of the type of surface the printed text comes from, just as long as each word, phrase, or set of numbers to be recognized is isolated, stored in a digital form, and then Fourier Transformed. The sine and cosine coefficients from the Fourier Transform are then filtered to include only the low frequency, terms (i.e. DC term and first 5 harmonics in both vertical and horizontal axis).

Abstract: The image of a word is taken and the two-dimensional discrete Fourier transform of the image is computed. The transformed image is filtered to the first three harmonics, with both real and imaginary components. These components then make up a total of 49 unique vectors which defines a 49 orthogonal vector space. The vector space is normalized to unity and each image of a word or phrase defines a point within this 49 orthogonal, hypersphere. The same process is done to the image for the Fourier components, where there is only 25 unique vector components. Similar looking words cluster in the hypersphere and the smaller distance from one point to another defines the probability of incorrectly recognizing a word. In a study for the case of two through eleven letters in a word using both 49 and 25 vector space calculations, the results show two through eleven words are recognizable using 49 vector space and possibly the 25 vector space.