Hand gesture recognition system helps in development of interface system for entering text in human computer interaction. In this paper, we have presented a hand gesture recognition system designed for dataset consisting of numerals and alphabets in lower case. The proposed system detects the hand with the help of skin color and motion information. Hand tracking is done with the help of two-level tracking system using modified Kanade–Lucas–Tomasi (KLT) tracking algorithm. The existing KLT was not able to track the gesture trajectory once the skin detected becomes less in area resulting in decreased number of points. In this paper, traditional KLT has been modified with a new additional feature to overcome this difficulty. In feature extraction process, a feature matrix consisting of 30 features have been created. Among these 30 features, few features like density-1, density-2, and perimeter efficiency have been introduced and are used for calculating efficiency along with some existing features. Inclusion of new features helps in improving the performance and accuracy of the system. Recognition is done using six classifiers including SVM (Support Vector machine), Decision Tree, Naïve Bayes, k-NN (K nearest neighbor), ANN (Artificial neural Network) and ELM (Extreme learning Machine). The experimental results prove that 89.67% of accuracy is achieved for the recognition of dataset containing both numerals and alphabets. Our proposed system is also compared with two existing literatures and it has been observed that better accuracy is exhibited by the proposed system.

手势识别系统有助于开发用于在人机交互中输入文本的界面系统。在本文中，我们提出了一种为由小写数字和字母组成的数据集设计的手势识别系统。所提出的系统在肤色和运动信息的帮助下检测手。手部跟踪是在两级跟踪系统的帮助下使用改进的 Kanade-Lucas-Tomasi (KLT) 跟踪算法完成的。一旦检测到的皮肤区域变小，导致点数减少，现有的 KLT 就无法跟踪手势轨迹。在本文中，传统的 KLT 已被修改为一个新的附加功能来克服这个困难。在特征提取过程中，已经创建了一个由 30 个特征组成的特征矩阵。在这 30 个特征中，引入了密度 1、密度 2 和周长效率等少数特征，并与一些现有特征一起用于计算效率。包含新功能有助于提高系统的性能和准确性。识别是使用六个分类器完成的，包括 SVM（支持向量机）、决策树、朴素贝叶斯、k -NN（K最近邻）、ANN（人工神经网络）和 ELM（极限学习机）。实验结果证明，对同时包含数字和字母的数据集的识别准确率达到了89.67%。我们提出的系统也与两个现有的文献进行了比较，并且已经观察到所提出的系统表现出更好的准确性。