Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a new approach for full page text recognition. Localization of the text lines is based on regressions with Fully Convolutional Neural Networks and Multidimensional Long Short-Term Memory as contextual layers. In order to increase the efficiency of this localization method, only the position of the left side of the text lines are predicted. The text recognizer is then in charge of predicting the end of the text to recognize. This method has shown good results for full page text recognition on the highly heterogeneous Maurdor dataset.
In the context of historical collection conservation and worldwide diffusion, this paper presents an automatic approach of historical book page layout segmentation. In this article, we propose to search the homogeneous regions from the content of historical digitized books with little a priori knowledge by extracting and analyzing texture features. The novelty of this work lies in the unsupervised clustering of the extracted texture descriptors to find homogeneous regions, i.e. graphic and textual regions, by performing the clustering approach on an entire book instead of processing each page individually. We propose firstly to characterize the content of an entire book by extracting the texture information of each page, as our goal is to compare and index the content of digitized books. The extraction of texture features, computed without any hypothesis on the document structure, is based on two non-parametric tools: the autocorrelation function and multiresolution analysis. Secondly, we perform an unsupervised clustering approach on the extracted features in order to classify automatically the homogeneous regions of book pages. The clustering results are assessed by internal and external accuracy measures. The overall results are quite satisfying. Such analysis would help to construct a computer-aided categorization tool of pages. In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we apply convolutional autoencoders to learn features directly from pixel intensity values. Then, using these features to train an SVM, we achieve high quality segmentation without any assumption of specific topologies and shapes. Experiments on three public datasets demonstrate the effectiveness and superiority of the proposed approach.