A sign language recognition system is an attempt to help the speech and the hearing-impaired community. The biggest challenge is to recognize a sign accurately. This can be achieved by training the computers to identify the signs. The accuracy depends on the methods used for classification and prediction which is achieved through machine learning. This research proposes the recognition of American Sign Language by using Support Vector Machine (SVM) and Convolutional Neural Network (CNN). In this work we have also calculated optimal filter size for single and double layer Convolutional Neural Network. In the first phase features from the dataset are extracted. After applying various preprocessing techniques, Support Vector Machine with four different kernels i.e., ‘poly’, ‘linear’, ‘rbf’ and ‘sigmoid’ and Convolutional Neural Networks with single and double layer are applied on training dataset to train the model. Finally, accuracy is calculated and compared for both the techniques. In CNN filters of different sizes have been used and optimal filter size has been found. The experimental results establish that the double layer Convolutional Neural Network achieve an accuracy of 98.58%. Optimal filter size is found out to be 8 × 8 for both single and double layer Convolutional Neural Network. From the experimental results we conclude that accuracy of CNN model can be improved by altering the filter size. This also helps in CNN to learn optimum values for variable sized parameters and tuning of different hyper parameters.

手语识别系统是试图帮助语音和听力受损社区的尝试。最大的挑战是准确识别标志。这可以通过训练计算机来识别标志来实现。准确性取决于通过机器学习获得的用于分类和预测的方法。这项研究提出了通过使用支持向量机（SVM）和卷积神经网络（CNN）来识别美国手语的方法。在这项工作中，我们还计算了单层和双层卷积神经网络的最佳滤波器大小。在第一阶段，从数据集中提取特征。应用各种预处理技术后，支持向量机将具有四个不同的内核，即“多边形”，“线性”，“ 将“ rbf”和“ Sigmoid”以及具有单层和双层的卷积神经网络应用于训练数据集以训练模型。最后，针对两种技术计算准确性并进行比较。在CNN中，使用了不同大小的过滤器，并且找到了最佳的过滤器大小。实验结果表明，双层卷积神经网络的准确率达到98.58％。发现最佳过滤器尺寸为单层和双层卷积神经网络都为8×8。从实验结果我们得出结论，可以通过更改滤波器大小来提高CNN模型的准确性。这也有助于CNN学习可变大小参数的最佳值以及不同超参数的调整。