Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields. While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of images, (c) dotted text printed on curved reflective material, and/or (d) touching characters. Methods were evaluated using a total of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10 seconds (using MATLAB R2008A on an HP 8510？W with 4？G of RAM and 2.3？GHz of processor speed), and experimental results yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development. 1. Introduction Recognition of degraded characters is a challenging problem in the field of image processing and optical character recognition (OCR). The accuracy and the efficiency of OCR applications are dependent upon the quality of the input image ？[1–3]. Security applications and data processing have increased the interest in this area dramatically. Therefore, the ability to replicate and distribute extracted data becomes more important ？[4, 5]. In [6], Jung et al. presented a survey of the term text information extraction (TIE) within an image, by assuming that there is no prior knowledge such as location, orientation, number of characters, font, color, and so on. They also noted that: (a) the variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging; (b) variety of approaches to TIE from images and video have been proposed for specific applications, such as page segmentation, address block location, license plate location, and content-based image/video indexing. In spite of such extensive studies, it still remains laborious to design a general-purpose OCR system [5] for the reason that there is an abundance of possible sources of variation when extracting text from a shaded or textured background, from low-contrast or complex images, or from images having variations in font size, style, color, and orientation [6]. These variations make the problem of automatic TIE extremely arduous and convoluted. Recently, many
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