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Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

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Anthropometric dimensions, such as lengths, heights, breadths, circumferences and their ratios are highly significant in healthcare, security, sports, clothing, tools and equipment industry. In this study, an automatic and precise method for anthropometric dimensions of human body using two-dimensional images is proposed. The dimensions are obtained by using fiducial points that are detected from frontal and lateral views of body silhouettes. Primary anthropometric dimensions, which include heights, breadths, depths and lengths, are obtained by calculating the difference between two relevant fiducial points. The secondary dimensions: ratios are obtained directly from primary dimensions, and circumference dimensions are estimated precisely using ellipsoid model. A total of 75, i.e. 51 primary and 24 secondary dimensions are obtained, which are three times the number acquired by the state-of-the-art method. The accuracy of acquired dimensions is verified by comparing it with the manual measurements by using the standard parameter of maximum allowable error. It is found that mean absolute difference of all the dimensions, obtained by the proposed method, lie within the limits of maximum allowable error. More importantly, the mean absolute difference for the majority of dimensions (20 out of 24) is significantly less for proposed method as compared with the best method in existing literature.

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