An effective DeepWINet CNN model for off-line text-independent writer identification.

Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has attracted much attention in recent decades due to the urgent need to develop biometric systems for many security applications. In this paper, Deep Writer Identification Network (DeepWINet), an effective deep Convolutional Neural Network (CNN) for writer identification, is proposed. The proposed model is evaluated in two different ways. In the first scenario, DeepWINet's CNN activation features, computed from the connected components of the writing, are passed to a customized nearest neighbor classifier for writer identification. In the second scenario, DeepWINet is evaluated as an end-to-end CNN network where the predicted results are averaged using an efficient strategy, Score Averaging Component-Decision Combiner. The proposed approach achieves competitive or the highest State-Of-The-Art performance on eight challenging handwritten databases with different languages. [ABSTRACT FROM AUTHOR]