Learning Disentangled Representations via Mutual Information Estimation

ISBN 978-3-030-58541-9International audienceIn this paper, we investigate the problem of learning disentangled representations. Given a pair of images sharing some attributes, we aim to create a low-dimensional representation which is split into two parts: a shared representation that captures the common information between the images and an exclusive representation that contains the specific information of each image. To address this issue, we propose a model based on mutual information estimation without relying on image reconstruction or image generation. Mutual information maximization is performed to capture the attributes of data in the shared and exclusive representations while we minimize the mutual information between the shared and exclusive representation to enforce representation disentanglement. We show that these representations are useful to perform downstream tasks such as image classification and image retrieval based on the shared or exclusive component. Moreover, classification results show that our model outperforms the state-of-the-art model based on VAE/GAN approaches in representation disentanglement

Direct unsupervised text line extraction from colored historical manuscript images using DCT

Extracting lines of text from a manuscript is an important preprocessing step in many digital paleography applications. These extracted lines play a fundamental part in the identification of the author and/or age of the manuscript. In this paper we present an unsupervised approach to text line extraction in historical manuscripts that can be applied directly to a color manuscript image. Each of the red, green and blue channels are processed separately by applying DCT on them individually. One of the key advantages of this approach is that it can be applied directly to the manuscript image without any preprocessing, training or tuning steps. Extensive testing on complex Arabic handwritten manuscripts shows the effectiveness of the proposed approach

Searching and browsing in historical documents ::state of the art and novel approaches for template-based keyword spotting

In many public and private institutions, the digitalization of handwritten documents has progressed greatly in recent decades. As a consequence, the number of handwritten documents that are available digitally is constantly increasing. However, accessibility to these documents in terms of browsing and searching is still an issue as automatic full transcriptions are often not feasible. To bridge this gap, Keyword Spotting (KWS) has been proposed as a flexible and error-tolerant alternative to full transcriptions. KWS provides unconstrained retrievals of keywords in handwritten documents that are acquired either online or offline. In general, offline KWS is regarded as the more difficult task when compared to online KWS where temporal information on the writing process is also available. The focus of this chapter is on handwritten historical documents and thus on offline KWS. In particular, we review and compare different state-of-the-art as well as novel approaches for template-based KWS. In contrast to learning-based KWS, template-based KWS can be applied to documents without any a priori learning of a model and is thus regarded as the more flexible approach