Wrist-wearable devices such as smartwatch have gained popularity as they provide quick access to the various information and easy access to multiple applications. Among various applications of the smartwatch, user verification based on the handwriting has been recently investigated. In this paper, we present a novel technique for user verification using a smartwatch based on writing pattern or signing pattern. The proposed technique leverages accelerometer data captured from the smartwatch that are further represented using 2D Continuous Wavelet Transform (CWT) and deep features extracted using the pre-trained ResNet50. The comparison is performed using the ensemble of the classifier. Extensive experiments are carried out on the newly captured dataset using two different smartwatches with three different writing scenarios (or activities). The article provides key insights and analysis of the results in such a verification scenario.

Methods developed for normal 2D text detection do not work well for a text that is rendered using decorative, 3D effects. This paper proposes a new method for classification of 2D and 3D natural scene images such that an appropriate method can be chosen or modified according to the complexity of the individual classes. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure we call COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed to a Neural Network (NN) for classification. The proposed method is tested on both a new dataset introduced in this work and a standard dataset assembled from different natural scene datasets, and compared to from existing methods to show its effectiveness. The approach improves text detection performance significantly after classification.