WriterINet: a multi-path deep CNN for offline text-independent writer identification

Handwriting-based identification is a fundamental pattern recognition problem that has attracted considerable interest in recent years. Writer identification is a major challenge, considering how diverse the written content is and how much handwriting differs between writers. This paper presents WriterINet, a CNN-based approach to learning and characterizing each writer’s writing style. The proposed WriterINet approach takes handwritten documents as input, decomposing each of them into word images and connected component sub-images. Each segmented image is then fed into our feature learning step to generate discriminative and deep features. To this end, we developed a powerful deep CNN feature architecture consisting of two CNN streams derived from fine-tuning the ResNet-50 and DenseNet-201 models. Deep learned features are computed from all segmented images representing the writer’s document and classified using a proposed 1D artificial neural network for predicting writer identification by averaging the similarity scores. Experimental results on IAM, ICDAR2013, CVL, IFN\(/\)ENIT, ICDAR2011, Firemaker and CERUG show that our WriterINet approach achieves the highest or competitive performance over the state-of-the-art.