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Abstract-Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of 52,688 images for the 23 common hand-shapes from ISL. Afterwards, we filter the redundant images with an iterative image selection process that selects the images which keep the dataset diverse. For classification, we use Principal Component Analysis (PCA) with with K-Nearest Neighbours (k-NN) and Convolutional Neural Networks (CNN). We obtain a recognition accuracy of 0.95 for our PCA model and 0.99 for our CNN model. We show that image blurring improves PCA results to 0.98. In addition, we compare times for classification.

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Abstract-In this work we use a new image dataset for Irish Sign Language (ISL) and we compare different approaches for recognition. We perform experiments and report comparative accuracy and timing. We perform tests over blurred images and compare results with non-blurred images. For classification, we use end-to-end approach, such as Convolutional Neural Networks (CNN) and feature based extraction approaches, such as Principal Component Analysis (PCA) followed by different classifiers, i.e. multilayer perceptron (MLP). We obtain a recognition accuracy over 99% for both approaches. In addition, we report different ways to split the training and testing dataset, being one iterative and the other one random selected.