Hybrid combination of Convolutional Neural Network Features for Robust Writer Retrieval of Historical Documents

The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.