Text Detection and Character Recognition Using Fuzzy Image Processing

The current investigation presents an algorithm and software to detect and recognize character in an image. Three types of fonts were under investigation, namely, case (I): Verdana, case (II): Arial and case (III): Lucida Console. The font size will be within the range of 17–29 font size. These types were chosen because the characters have low variance and there is less redundancy in the single character. Also, they have no breakpoints in the single character or merge in group of characters as noticed in Times New Roman type. The proposed algorithm assumed that there are at least three characters of same color in a single line, the character is on its best view which means no broken point in a single character and no merge between group of characters and at last, a single character has only one color. The basic algorithm is to use the 8-connected component to binirize the image, then to find the characters in the image and recognize them. Comparing this method with other methods, we note that the main difference is in the binarizing technique. Previous work depends mainly on histogram shape. They calculate the histogram, and then smooth it to find the threshold points. These methods are not working well when the text and background colors are very similar, and also if it may create an area of negative text. The negative text may be treated as a graphic region which may affect the system efficiency. The presented approach will take each color alone. This will make the merge between the text and the background unlikely to happen. Also there will be no negative text in the whole image because the negative text to a color will be normal text for another. The shown and discussed results will show the efficiency of the proposed algorithm and how it is different compared with other algorithms.