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Existing technologies identify individual lines in a line plot by matching the symbols in the legend. Identifying each symbol is crucial to create accessible graphics for people who are blind so that they provide the best resemblance possible to the original plot. This resemblance provides better communication between sighted and vision impaired users. In our work we use an existing network architecture and improve it in a way that it will get more semantic information out of the lines and their symbology. We present these line graphs in the GraVVITAS system, an approach for providing dynamic and refreshable accessible graphics, to be used by people who are blind.

In this paper we present a method for text recognition in floor plan images. In particular, we are concerned about locating, reading, and categorizing text inside floor plan images to obtain information about the building. Furthermore, the aim of this paper is to compare traditional text detection methods, based on image processing techniques, with recent approaches relying on convolutional neural networks. To improve results we combined several methods outperforming the original ones. Text regions are also classified in four semantic classes according to their purpose. Two datasets with different features, including quality and size, were considered in the experiments performed.

We are developing an application to automatically generate an accessible graphic from a floor plan image. Floor plans generally contain large regions with functionally different sub-areas. A problem faced by visually impaired users in exploring such accessible floor plans is understanding the boundaries of these sub-areas. We present an effective method to partition such open plan areas. Initially, we conducted a formative user study to understand how people partition open plan areas. Based on the findings of the study, we identified a general set of guidelines for partitioning open plans. These guidelines were used to generate a set of candidate lines for sub-areas. An obstacle avoiding shortest-path Voronoi diagram was used to determine boundaries for each sub-area. Candidate lines such as wall extensions were automatically generated to replace the identified boundaries. We selected the best replacement for each boundary by scoring candidate lines using a set of criteria such as line length. Finally the proposed method was tested on a standard floor plan corpus using three novel measures.

This paper presents a deep Convolutional Neural Network (CNN) based approach for document image classification. One of the main requirement of deep CNN architecture is that they need huge number of samples for training. To overcome this problem we adopt a deep CNN which is trained using big image dataset containing millions of samples i.e., ImageNet. The proposed work outperforms both the traditional structure similarity methods and the CNN based approaches proposed earlier. The accuracy of the proposed approach with merely 20 images per class outperforms the state-of-the-art by achieving classification accuracy of 68.25%. The best results on Tobbacoo-3428 dataset show that our proposed method outperforms the state-of-the-art method by a significant margin and achieved a median accuracy of 77.6% with 100 samples per class used for training and validation.

Information graphics are common in written communication. The ability to comprehend, use and create these graphics is an important skill that most of us take for granted. However, for those who are visually impaired, access to such graphics is severely limited. While new presentation technologies will improve access, an unsolved problem is how to automatically generate an accessible version of an information graphic. We describe the goals and approach of a project to develop software tools to do this. Our proposed approach uses advanced graphics recognition combined with high-level transformation. It is designed to handle a wide variety of information graphics, including floor plans, line graphs and organisation charts, and to be usable in schools, workplaces and at home.

In this paper, the Earth Mover's Distance (EMD) is used as a similarity measure in the mathematical symbol retrieval task. The approach is based on the Bag-of-Visual-Words model. In our case the features extracted from each symbol are clustered by means of Self-Organizing Maps (SOM) and then occurrences of features in the clusters are accumulated in a vector of visual words. The comparison between the latter vectors is performed with the EMD which naturally allows to incorporate the topological organization of SOM clusters in the distance computation. The proposed approach is experimentally tested in a mathematical symbol retrieval task and compared with the cosine similarity and with some variants that have been recently proposed.

In the last years the interest in e-book readers is significantly growing. Two main document formats are supported by most devices: PDF and ePub. The PDF format is widely used to share documents allowing a cross-platform readability. However, it is not ideal for a comfortable reading on small screens. On the opposite, the ePub format is re-flowable and it is well suited for e-book readers. In this paper we describe a system for the conversion of PDF books to the ePub format aiming at inverting the text formatting made during the pagination. To this purpose, layout analysis techniques are performed to identify the book's table of contents and the main functional regions such as chapters, paragraphs, and notes.

In this paper, we describe a general approach for script (and language) recognition from printed documents and for writer identification in handwritten documents. The method is based on a bag of visual word strategy where the visual words correspond to characters and the clustering is obtained by means of Self Organizing Maps (SOM). Unknown pages (words in the case of script recognition) are classified comparing their vectorial representations with those of one training set using a cosine similarity. The comparison is improved using a similarity score that is obtained taking into account the SOM organization of cluster centroids. % Promising results are presented for both printed documents and handwritten musical scores.

In this paper we analyze our recent research on the use of document analysis techniques for metadata extraction from PDF papers. We describe a package that is designed to extract basic metadata from these documents. The package is used in combination with a digital library software suite to easily build personal digital libraries. The proposed software is based on a suitable combination of several techniques that include PDF parsing, low level document image processing, and layout analysis. In addition, we use the information gathered from a widely known citation database (DBLP) to assist the tool in the difficult task of author identification. The system is tested on some paper collections selected from recent conference proceedings.

This paper addresses the indexing and retrieval of mathematical symbols from digitized documents. The proposed approach exploits Shape Contexts (SC) to describe the shape of mathematical symbols. Starting from the vector space method, that is based on SC clustering, we explore the use of topological ordered clusters to improve the retrieval performance. The clustering is computed by means of Self-Organizing Maps that organize the clusters in two dimensional topologically ordered feature maps. The retrieval performance are compared with those obtained using the K-means clustering on a large collection of mathematical symbols gathered from the widely used INFTY database.

In this paper we compare three clustering methods used to perform word image indexing. The three methods are: the Self-Organizing Map (SOM), the Growing Hierarchical Self-Organizing Map (GHSOM), and the Spectral Clustering. We test these methods on a real data set composed of word images extracted from an encyclopedia of the XIX-th Century. The word images are grouped on the basis of the clustering methods and subsequently retrieved identifying the closest clusters to a query word. The accuracy of the methods is compared evaluating the performance of the word retrieval algorithm. From the experimental results we conclude that methods designed to automatically determine the number and the structure of clusters, such as GHSOM, are particularly suitable in the context represented by our data set.

We describe a system for the retrieval on the basis of layout similarity of document images belonging to collections stored in digital libraries. Layout regions are extracted and represented with the XY tree. The proposed indexing method combines a new tree clustering algorithm (based on self organizing maps) with principal component analysis. The combination of these techniques allows us to retrieve the most similar pages from large collections without the need for a direct comparison of the query page with each indexed document.

We analyze a system for the retrieval of document images on the basis of layout similarity. Layout objects are extracted and represented with the XY tree. Page similarity is computed with a tree-edit distance algorithm. The peculiarity of the approach is the use of tree grammars to model the variations in the tree, which are due to segmentation algorithms or to structural differences between documents with similar layout. A few class-independent grammatical rules are used to modify each tree and obtain a reduced tree that is supposed to preserve the most relevant features of the page.

Large collections of scanned documents (books and journals) are now available in digital libraries. The most common method for retrieving relevant information from these collections is image browsing, but this approach is not feasible for books with more than a few dozen pages. The recognition of printed text can be made on the images by OCR systems, and in this case a retrieval by textual content can be performed. However, the results heavily depend on the quality of original documents. More sophisticated navigation can be performed when an electronic table of contents of the book is available with links to the corresponding pages. An opposite approach relies on the reduction of the amount of symbolic information to be extracted at the storage time. This approach is taken into account by document image retrieval systems. We describe a system that we developed in order to retrieve information from digitized books and journals belonging to digital libraries. The main feature of the system is the ability of combining two principal retrieval strategies in several ways. The first strategy allows an user to find pages with a layout similar to a query page. The second strategy is used in order to retrieve words in the collection matching a user-defined query, without performing OCR. The combination of these basic strategies allows users to retrieve meaningful pages with a low effort during the indexing phase. We describe the basic tools used in the system (layout analysis, layout retrieval, word retrieval) and the integration of these tools for answering complex queries. The experimental results are made on 1287 pages and show the effectiveness of the integrated retrieval.

Describes the positioning module of a visual inspection system for dimensional verification. The system operates in two phases: learning and test. In the learning phase, the operator performs a set of inspection operations on the image of the prototype piece, while in the test phase the system processes the image of a piece that belongs to the class considered in the learning phase. For this purpose, the position of the test piece has to be determined. The authors represent the prototype and test pieces by attributed relational graphs (ARG) in which the nodes describe closed contours while the arcs represent their mutual relationships. Positioning is obtained by means of a graph matching technique based on a hypothesize and verify cycle. The authors use a hierarchical approach for graph matching; first they look for node correspondences (operating on a contour matching level), then they find the graph match by hypothesizing an arc correspondence and evaluating its consequences on the whole graph. >

We describe a top-down approach to the segmentation and representation of documents containing tabular structures. Examples of these documents are invoices and technical papers with tables. The segmentation is based on an extension of X-Y trees, where the regions are split by means of cuts along separators (e.g. lines), in addition to cuts along white spaces. The leaves describe regions containing homogeneous information and cutting separators. Adjacency links among leaves of the tree describe local relationships between corresponding regions.