Automated sign language recognition is one of the important areas of computer vision today, because of its applicability in vast fields of life. This paper presents automated recognition of signs taken from Pakistani Sign Language (PSL). The paper presents empirical analysis of two statistical and one transformation based shape descriptors for the recognition of PSL. A purely vision based, efficient, signer independent, multi-aspect invariant method is proposed for the recognition of 44 signs of PSL. The method has proved its worth by utilizing a very small shape descriptor and giving promising results for a reasonable size of sign dictionary. The proposed methodology achieved an accuracy of 92%. 

With the dramatic increase in multimedia data, escalating trend of internet, and amplifying use of image/video capturing devices; content based indexing and text extraction is gaining more and more importance in research community. In the last decade, many techniques for text extraction are reported in the literature. Methodologies of text extraction from images/videos is generally comprises of text detection and localization, text tracking, text segmentation and optical character recognition (OCR). This paper intends to highlight the contributions and limitations of text detection, localization and tracking phases. The problem is exigent due to variations in the font styles, size and color, text orientations, animations and backgrounds. The paper can serve as the beacon-house for the novice researchers of the text extraction community.