Offline text recognition without intraword character segmentation based on two-dimensional low frequency discrete Fourier transforms

摘要

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). The sine and cosine terms (coefficients) then define 121 unique vectors which represent a 121 orthogonal vector space. The vector space is normalized to unity and each image of the word, phrase, or set of numbers defines a unique point along this 121 orthogonal vector hypersphere. A library of words, phrases, and/or numbers must be produced using many different font styles. The library when developed, will consist of sine and cosine coefficient values which represent each word, phrase, or number to be recognized. This library is uniquely fashioned by averaging the sine and cosine terms of similar font styles into what is called font groups.