Deaf and dumb people use sign language as a tool for communication. As per the 2011 census in India, hearing impaired and speech disabled population is 1,998,692 and 5,072,914 respectively (Disabled persons in India, https://www.mospi.gov.in). Normal hearing people do not learn sign language and thus there is a big communication gap between them and deaf and dumb people. Sign language interpreters can fill this gap but it is a very costly affair to hire them. Indian Sign Language (ISL) consists of signs which are made with two hands while other sign languages like American Sign Language consists of signs made with single hand. This work proposes an automatic and efficient computer vision based system to recognize ISL alphabet which can assist this communication. It can further be used as a module of complete ISL recognition system. Phases in ISL alphabet recognition are image acquisition, preprocessing, segmentation, feature extraction and classification. All 26 ISL alphabet have been considered for testing with average accuracy of 80.76%. Results show that the accuracy of 100% is achieved when similar alphabet {C, L, M, N, R, U,Y} are excluded from testing dataset.

聋哑人使用手语作为交流的工具。根据印度2011年的人口普查，有听力障碍和语言障碍的人口分别为1,998,692和5,072,914（印度的残疾人，https：//www.mospi.gov.in）。正常听力的人不会学习手语，因此他们与聋哑人之间的沟通差距很大。手语翻译人员可以填补这一空白，但雇用他们是一件非常昂贵的事情。印度手语（ISL）包括用两只手制作的符号，而其他手语（如美国手语）则用一只手制作的符号组成。这项工作提出了一种基于自动和高效的计算机视觉的系统，以识别可以帮助这种通信的ISL字母。它可以进一步用作完整的ISL识别系统的模块。ISL字母识别的阶段包括图像获取，预处理，分割，特征提取和分类。已考虑所有26个ISL字母进行测试，平均准确度为80.76％。结果表明，从测试数据集中排除相似的字母{C，L，M，N，R，U，Y}时，可以达到100％的精度。