In this research paper, an effort has been placed to convert 24 American Sign Language (ASL) signer independent, real time hand gesture alphabets into human or machine recognizable English text. In the proposed work, the ASL hand gestures used for cognition and recognition process is completely invariant to scale, luminance, gender, and distance in the complex background of indoor location. The Viola-Jones algorithm, CIE Lab color model and canny approximation to the derivative are used for proper hand segmentation. In both the cognition and recognition process, the various features such as boundary, centroid, entropy, Hu moments, Zernike moments, Gabor filters, Histogram of Oriented Gradients (HOG) and Local Phase Quantization (LPQ) are extracted from the hand gestures. The K-Nearest Neighbor (KNN), Multiclass-Support Vector Machines (M-SVM) and Decision Tree (DT) classifiers are used for classifying the hand gestures. In recognition task, these classifiers are applied independently on the same set of hand gestures to check the optimality of recognition rate and recognition time. With the detailed experimentation, it is found that, the KNN classifier achieved an average recognition rate and average recognition time of 92.71% and 0.48 s per gestures. This recognition rate and time is better and optimal compared to M-SVM and DT classifiers. Also it is an inspiring result compared to state of art techniques in real time environment by considering various invariants.

在本研究论文中，已努力将24种独立于美国手语（ASL）签名者的实时手势字母转换为人或机器可识别的英文文本。在拟议的工作中，用于认知和识别过程的ASL手势在复杂的室内位置背景下的比例，亮度，性别和距离完全不变。使用Viola-Jones算法，CIE Lab颜色模型和导数的Canny近似进行正确的手部分割。在认知和识别过程中，都从手势中提取了各种特征，例如边界，质心，熵，Hu矩，Zernike矩，Gabor滤波器，定向梯度直方图（HOG）和局部相位量化（LPQ）。K最近邻居（KNN），多类支持向量机（M-SVM）和决策树（DT）分类器用于对手势进行分类。在识别任务中，将这些分类器独立应用于同一组手势，以检查识别率和识别时间的最优性。通过详细的实验发现，KNN分类器的平均识别率和平均识别时间分别为92.71％和0.48 s /手势。与M-SVM和DT分类器相比，此识别率和时间更好，更优化。通过考虑各种不变量，与实时环境中的最新技术水平相比，这也是一个令人鼓舞的结果。这些分类器分别应用于同一组手势，以检查识别率和识别时间的最优性。通过详细的实验发现，KNN分类器的平均识别率和平均识别时间分别为92.71％和0.48 s /手势。与M-SVM和DT分类器相比，此识别率和时间更好，更优化。通过考虑各种不变量，与实时环境中的最新技术水平相比，这也是一个令人鼓舞的结果。这些分类器分别应用于同一组手势，以检查识别率和识别时间的最优性。通过详细的实验发现，KNN分类器的平均平均识别率和平均识别时间分别为92.71％和0.48 s /手势。与M-SVM和DT分类器相比，此识别率和时间更好，更优化。通过考虑各种不变量，与实时环境中的最新技术水平相比，这也是一个令人鼓舞的结果。