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International Journal of Computer Vision & Signal Processing (ISSN: 2186-1390)  


Vol. 3, No. 1:

Alignment of Curved Text Strings for Enhanced OCR Readability

T. Kasar, Universite de Rouen, France
A. G. Ramakrishnan, Indian Institute of Science, India

A Developed System for Melanoma Diagnosis

Nadia Smaoui, National School of Engineers of Sfax, Tunisia
Souhir Bessassi, Higher Institute of Computer Science and Multimedia of Gabes, Tunisia

Alignment of Curved Text Strings for Enhanced OCR Readability

Abstract:
Conventional optical character recognition systems, designed to recognize linearly aligned text, perform poorly on document images that contain multi-oriented text lines. This paper describes a novel technique that can extract text lines of arbitrary curvature and align them horizontally. By invoking the spatial regularity properties of text, adjacent components are grouped together to obtain the text lines present in the image. To align each identified text line, we fit a B-spline curve to the centroids of the constituent characters and normal vectors are computed all along the resulting curve. Each character is then individually rotated such that the corresponding normal vector is aligned with the vertical axis. The method has been tested on images that contain text laid out in various forms namely arc, wave, triangular and combination of these with linearly skewed text lines. It yields 97.3% recognition accuracy on text strings where state-of-the-art OCRs fail before alignment.
Full Paper (in PDF)

A Developed System for Melanoma Diagnosis

Abstract:
In recent years, there has been a fairly rapid increase in the number of melanoma skin cancer patients. Melanoma, this deadliest form of skin cancer, must be diagnosed early for effective treatment. So, it is neces- sary to develop a computer-aided diagnostic system to facilitate its early detection. In this paper, the proposed work is based on a combination of a segmentation method and an analytical method and aims to im- prove these two methods in order to develop an interface that can assist dermatologists in the diagnostic phase. As a first step, a sequence of preprocessing is implemented to remove noise and unwanted structures from the image. Then, an automatic segmentation approach locates the skin lesion. The next step is feature extraction followed by the ABCD rule to make the diagnosis through the calculation of the TDV score. In this research, three diagnosis are used which are melanoma, suspicious, and benign skin lesion. The experiment uses 40 images containing sus- picious melanoma skin cancer. Based on the experiment, the accuracy of the system is 92% which reflects its viability.
Full Paper (in PDF)

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