Identification of person from his writing is one of the challenging problem, however is not new. No one can repudiate its applications in number of domains such as, forensic analysis, historical documents and ancient manuscripts etc. Deep learning based approaches have proved as the best features extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compare to traditional approaches.We apply deep transfer Convolutional Neural Network (CNN) to identity writer using handwriting text line images in English and Arabic languages.We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7 and fusion of Fc6 and Fc7) affecting the identification rate of writer. In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set). To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives and sharpness using text-line images of target data-set. The sliding window approach is used to make patches as an input unit to the CNN model. The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques. The extracted features from patches are then fed to a Support Vector Machine (SVM) classifier.We realized highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic and 88.11% on combination of Arabic and English, respectively.

Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on a deep learning framework show promising results. In this paper, we present several end-to-end CNN-based deep learning architectures, i.e., AlexNet, VGG16/19, GoogLeNet, ResNet50.101, MobileNet, SqueezeNet, and DenseNet, for infection and ischemia categorization using the benchmark dataset DFU2020. We fine-tune the weight to overcome a lack of data and reduce the computational cost. Affine transform techniques are used for the augmentation of input data. The results indicate that the ResNet50 achieves the highest accuracy of 99.49% and 84.76% for Ischaemia and infection, respectively.