Static Pakistani Sign Language Classification using Support Vector Machine

In this study, a system is proposed that uses the Support Vector Machine (SVM) technique with Bag-of-Words (BoW) and recognizes static Pakistani Sign Language (PSL) alphabets. The application of the BoW technique with SVM, on a PSL images' dataset, has not been performed previously. Similarly, no publicly available dataset for PSL is available and previous studies have achieved a maximum classification accuracy of 91.98%. For this study, a total of 511 images are collected for 36 static PSL alphabet signs from a native signer. The Sign Language (SL) recognition system uses the collected images as input and converts them to grayscale. To segment the images, the system uses the thresholding technique and Speeded Up Robust Feature (SURF) to extract the features. The system uses K-means clustering to cluster the extracted features. To form the BoW, the system computes the Euclidean distance among SURF descriptors and clustered data. The system then uses 5-fold cross-validation to divide the codebooks obtained from the BoW into training and testing. The developed system yields an overall accuracy of 97.87% for the classification of static PSL signs at 1,500×1,500 image dimensions and 500 Bags.