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This paper presents an image processing technique for mapping Bangla Sign Language alphabets to text. It attempts to process static images of the subject considered, and then matches them to a statistical database of pre-processed images to ultimately recognize the specific set of signed letters. Hand gesture recognition is a challenging problem in its general form. We consider a fixed set of manual commands and a reasonably structured Environment, and develop a simple, yet effective, procedure for gesture recognition. Our approach contains steps for converting the RGB image to Binary image, removing noise from this image, segmenting the hand region, finding out its area, circumference, and edges then extract some features from this preprocessed image. Then we create a database based on this features and classify the gesture based on the database. We also use exclusive-Or template matching and PSNR (Peak signal to noise ratio) comparison to detect the signs of Bangla Sign Language. Finally we combine the result of these three methods to detect the resultant gesture and convert them to text. We demonstrate the effectiveness of the technique on real imagery.