Training Convolutional Autoencoders with Metric Learning We propose a new Training method that enables an autoencoder to extract more useful features for retrieval or classification tasks with limitedsize datasets. Some targets in document analysis and recognition (DAR) including signature verification, historical document analysis, and scene text recognition, involve a common problem in which the size of the dataset available for training is small against the intraclass variety of the target appearance. Recently, several approaches, such as variational autoencoders and deep metric learning, have been proposed to obtain a feature representation that is suitable for the tasks. However, these methods sometimes cause an overfitting problem in which the accuracy of the test data is relatively low, while the performance for the training dataset is quite high. Our proposed method obtains feature representations for such tasks in DAR using convolutional autoencoders with metric learning. The accuracy is evaluated on an imagebased retrieval of ancient Japanese signatures.

A Comprehensive Study of ImageNet PreTraining for Historical Document Image Analysis Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pretrain models on images from a different domain and then finetune them on historical documents. In the current research, a typical example of such crossdomain transfer learning is the use of neural networks that have been pretrained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pretraining helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and contentbased retrieval. While we obtain mixed results for semantic segmentation at pixellevel, we observe a clear trend across different network architectures that ImageNet pretraining has a positive effect on classification as well as contentbased retrieval.