A lexicon-free approach for 3D handwriting recognition using classifier combination
Pattern Recognition Letters
Recent developments in depth sensing technology such as Leap Motion have opened novel directions in Human-Computer-Interaction (HCI) research domain. The sensor extends the way of writing from traditional method to a gesture based writing in the 3D space. The online text written in 3D space over the sensorâs viewing field is different from traditional 2D handwriting in several ways. The 3D handwriting does not consist any stroke information, since all characters are connected by a single stroke. Moreover, non-uniform text styles and jitters during writing in 3D space create additional challenge for the recognition task. Because of these challenges in 3D handwriting, recognition of cursive words is not satisfied using a single classifier. In this paper, we present a lexicon free approach for the recognition of 3D handwritten words in Latin and Devanagari scripts by combining multiple classifiers. The individual recognition systems are computed using Bidirectional Long-Short Term Memory Neural Network (BLSTM-NN) classifier with the help of different features. The combination of multiple classifier is performed by aligning the output word sequence of each classifier using the Recognizer Output Voting Error Reduction (ROVER) framework. Accuracies of 72.25% and 71.86% are recorded using the proposed methodology for Latin and Devanagari scripts, respectively.
Kumar, Pradeep; Saini, Rajkumar; Roy, Partha Pratim; and Pal, Umapada, "A lexicon-free approach for 3D handwriting recognition using classifier combination" (2018). Journal Articles. 1495.