Deaf people or people with hearing loss have a major problem in everyday communication. There are many applications available in the market to help blind people to interact with the world. Voice-based email and chatting systems are available to communicate with each other by blinds. This helps to interact with persons by blind people. Also, many attempts have been made with Sign Language (SL) translators to solve of communication gap between normal and deaf people and ease communication for deaf people. In this paper, the geometric feature is used as feature extraction for static Sign recognition. Support Vector Machine (SVM) classifier is used for training and testing to develop a system using static Signs. So, the accuracy result for static Signs using the Geometric feature is 62. 92\% which needs to be improved by other feature extraction and classifiers.

According to the dominant view in linguistics, the relationship between Signifier and Signified is basically conventional and arbitrary. In this article we are going to claim that in home Sign Languages there are some degrees of motivation. To do this, we have collected the Signs of a home Signer through interview sessions within two years. After that, the Signs and the potential relations between Signifiers and Signifieds have been analyzed. All the similarities or any kind of meaningful relation between Signifiers and Signifieds have been pointed out and a list of motivation devices has been presented. The research shows that there is a logical and meaningful relation between almost all the Signifiers and the corresponding Signifieds which can be clearly seen in the light of motivation devices.

The polite listener In England stares at the speaker attentively and blinks his eyes occasionally as a Sign of interest. That eye-blink says nothing to Americans, who expect the listener to nod or to murmur something such as "mm - hmm”. And in some parts of  Far East, it is impolite to look at the other person at all during conversation.It is often argued that, in teaching foreign Languages, culture and Language are interwoven in a way that it is not possible to present Language without its culture. However, until a new method of teaching Language, called communicative approach, which is sociolinguistically oriented, had not come into existence, this belief was not used to be observed in textbooks and Language classes. This method intends to create situations in the classroom in which foreign Language can be used as naturally and authentically as the native speakers of target Language use it for communicative purposes.The problem of teaching culture, nevertheless, does not end here. Nowadays, foreign Languages are taught with different and varied objectives. Is it necessary for all foreign Language learners to get familiar with the culture of foreign Language they are supposed to learn? As Schumann (1984) claims, is it true that second Language learning is impossible, unless one gives up his/her own native culture and adopts the culture of foreign Language community? What are some relevant issues regarding teaching English in Iran? These are actually some basic questions raised in this article. Whereas definite answers are not necessarily provided in this paper, some areas of research related to culture, and teaching foreign Language are put forward.

There are many reports of using the Kinect to detect hand and finger gestures after release of device by Microsoft. The depth information is mostly used to separate the hand image in the two-dimension of RGB domain. This paper proposes a method in which the depth information plays a more dominant role. Using a threshold in depth space first the hand template is extracted. Then in 3D domain the perpendicular vector to the hand surface is found. Using the rotation matrix all the rotations along three axes are compensated in a way that the camera z-coordinate lies perpendicular to hand surface. Then the resulted 3d image is translated to a distance of 80 to 100 cm from the Kinect. Wavelet transform with a new descriptor, called Circular Descriptor are used to extract required features. A trained MLP neural network in conjunction with a SVM is used to classify the Signs. Empirical results show an average accuracy of 96. 7 % with a two seconds delay for online recognition of Persian Sign Language.