Hybrid DBLSTMSVM Based BetaEllipticCNN Models for Online Arabic Characters Recognition The deep learningbased approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid BetaElliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long shortterm memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of featurebased systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and OnlineKHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the online and offline systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other stateoftheart systems.

Exploration of CNN Features for Online Handwriting Recognition Recently, convolution neural network (CNN) has demonstrated its powerful ability in learning features particularly from image data. In this work, its capability of feature learning in online handwriting is explored, by constructing various CNN architectures. The developed CNNs can process online handwriting directly unlike the existing works that convert the online handwriting to an image to utilize the architecture. The first convolution layer accepts the sequence of (x; y) coordinates along the trace of the character as an input and outputs a convolved filtered signal. Thereafter, via alternating steps of convolution and Rectified Linear Unit layers, in a hierarchical fashion, we obtain a set of deep features that can be employed for classification. We utilize the proposed CNN features to develop a Support Vector Machine (SVM)based character recognition system and an implicitsegmentation based large vocabulary word recognition system employing hidden Markov model (HMM) framework. To the best of our knowledge, this is the first work of its kind that applies CNN directly on the (x; y) coordinates of the online handwriting data. Experiments are carried out on two publicly available English online handwritten database: UNIPEN character and UNIPEN ICROW03 word databases. The obtained results are promising over the reported works employing the pointbased features.