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.

基于手写识别的书写者识别被认为是模式识别和生物识别领域最常见的研究领域之一。近几十年来，由于许多安全应用迫切需要开发生物识别系统，它引起了广泛关注。本文提出了深度作家识别网络（DeepWINet），这是一种用于作家识别的有效深度卷积神经网络（CNN）。所提出的模型以两种不同的方式进行评估。在第一个场景中，DeepWINet 的 CNN 激活特征（根据写作的连接组件计算得出）被传递到定制的最近邻分类器以进行作者识别。在第二种情况下，DeepWINet 被评估为端到端 CNN 网络，其中使用有效策略对预测结果进行平均，分数平均组件决策组合器。所提出的方法在八个具有挑战性的不同语言的手写数据库上实现了具有竞争力或最高的最先进性能。