Word spotting using deep Convolutional Neural Networks (CNN) has recently obtained significant results in handwritten documents retrieval application. In this paper, we propose a novel word spotting method based on Monte-Carlo dropout CNN to compute the certainty of extracted features that can be used in both query-by-example (QBE) and query-by-string (QBS) word spotting scenarios. In the QBE and during the training, an adaptable certainty threshold is assigned for the words of each class. Cosine distance between the predicted certainty of the query image and the retrieval set is compared with the certainty threshold of each class in the matching step. For the QBS, the query class is compared to the class of the retrieval set obtained by the certainty prediction. We evaluated our proposed method on four public handwritten databases. Experimental results showed that the accuracy achieved in both QBE and QBS scenarios outperforms the state-of-the-art methods.

使用深度卷积神经网络（CNN）的单词发现最近在手写文档检索应用程序中获得了重要成果。在本文中，我们提出了一种基于蒙特卡洛辍学CNN的新颖单词发现方法，以计算提取的特征的确定性，该特征既可以用于示例查询（QBE）也可以用于按字符串查询（QBS）单词发现场景。在QBE中和训练期间，为每个班级的单词分配了一个适应性确定性阈值。在匹配步骤中，将查询图像的预测确定性与检索集之间的余弦距离与每个类别的确定性阈值进行比较。对于QBS，将查询类别与通过确定性预测获得的检索集的类别进行比较。我们在四个公共手写数据库上评估了我们提出的方法。