This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions possess neither standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast ICA based analysis fail to extract text from these inscription images. Natural gradient flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%), respectively. 1. Introduction A significant amount of research has been carried out in the direction of reading inscriptions from monuments around the world. Several methods have been proposed for detection of text, localization and extraction of text from images of inscriptions [1, 2]. But, the problem of text extraction intensifies when the difference in the text (foreground) and the background is very marginal, the background is textured, or the background and foreground are similar. Such is the case of camera-held images of inscriptions at the sites of historical monuments. Figure 1 shows an image of inscription found in world heritage site “Hampi.” These inscriptions are generally found engraved into/projected out from, stone, or other durable materials. However, due to effects of uncontrolled illuminations, wrapping, multilingual text, minimal difference between foreground and background images, and the distortion due to perspective projection as well as the complexity of image background, extracting text from these images is a challenging problem. Figure 1: Inscription found at Hampi. The commercially available Optical Character Recognition (OCR) has very poor recognition accuracy of images of the inscriptions on monuments. The images of English inscriptions from the monuments were passed through the commercial OCR for text extraction, but the OCR failed to recognize these images. These images can be recognized by OCR only after proper enhancement. Fast ICA [3] based enhancement method has given good results for inscription images with a reasonable colour difference between text and the background. Most of the ancient inscriptions do not have such reasonable colour
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