Deformation Classification of Drawings for Assessment of VisualMotor Perceptual Maturity Sketches and drawings are popularly employed in clinical psychology to assess the visualmotor and perceptual development in children and adolescents. Drawn responses by subjects are mostly characterized by high degree of deformations that indicates presence of various visual, perceptual and motor disorders. Classification of deformations is a challenging task due to complex and extensive rule representation. In this study, we propose a novel technique to model clinical manifestations using Deep Convolutional Neural Networks (DCNNs). Drawn responses of nine templates used for assessment of perceptual orientation of individuals are employed as training samples. A number of defined deviations scored in each template are then modeled by applying fine tuning on a pretrained DCNN architecture. Performance of the proposed technique is evaluated on samples of 106 children. Results of experiments show that pretrained DCNNs can model and classify a number of deformations across multiple shapes with considerable success. Nevertheless some deformations are represented more reliably than the others. Overall promising classification results are observed that substantiate the effectiveness of our proposed technique.

CNNBLSTMCRF Network for Semantic Labeling of Students Online Handwritten Assignments Automatic semantic labeling of strokes in online handwritten documents is a crucial task for many applications such as diagram interpretation, text recognition, and search. We formulate this task as a stroke classification problem in which each stroke is classified as a crossout, free body diagram, or text. Separating free body diagram and text in this work is different than the traditional textnontext separation problem because these two classes contain both text and graphics. The text class includes textual notes, mathematical symbolsequations, and graphics such as arrows that connect other elements. The free body diagram class also contains graphics and various alphanumeric characters and symbols that mark or explain the graphical objects. In this work, we present a novel deep neural network model for classification of strokes in online handwritten documents. There are two input sequences to the network. The first sequence contains the trajectories of the pen strokes while the second contains features of the strokes. Each of these sequences is fed to its own CNNBLSTM channel to extract features and encode relationships between nearby strokes. The output of the two channels is concatenated and used as the input to a CRF layer that predicts the best sequence of labels for given input sequences. We evaluated our model on a dataset of 1,060 pages written by 132 students in an undergraduate statics course. Our model achieved an overall classification accuracy of 94.70% on this dataset.