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Alicia Fornes and Bart Lamiroy. 2018. Graphics Recognition, Current Trends and Evolutions. Springer International Publishing. (LNCS.)
Abstract: This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza and Alicia Fornes. 2018. Optical Music Recognition by Long Short-Term Memory Networks. In A. Fornes, B.L., ed. Graphics Recognition. Current Trends and Evolutions. Springer, 81–95. (LNCS.)
Abstract: Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Keywords: Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
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Debora Gil and 7 others. 2017. Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer. 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging.151–159. (LNCS.)
Abstract: Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
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Pau Riba, Josep Llados and Alicia Fornes. 2017. Error-tolerant coarse-to-fine matching model for hierarchical graphs. In Pasquale Foggia, Cheng-Lin Liu and Mario Vento, eds. 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition. Springer International Publishing, 107–117.
Abstract: Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
Keywords: Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
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Veronica Romero, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2017. Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology. In L.A. Alexandre, J.Salvador Sanchez and Joao M. F. Rodriguez, eds. 8th Iberian Conference on Pattern Recognition and Image Analysis.287–294. (LNCS.)
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
Keywords: Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
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Juan Ignacio Toledo, Sebastian Sudholt, Alicia Fornes, Jordi Cucurull, A. Fink and Josep Llados. 2016. Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer International Publishing, 543–552. (LNCS.)
Abstract: The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.
Keywords: Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection
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Hana Jarraya, Muhammad Muzzamil Luqman and Jean-Yves Ramel. 2017. Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition. In B. Lamiroy and R Dueire Lins, eds. Graphics Recognition. Current Trends and Challenges. Springer. (LNCS.)
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Pau Riba, Alicia Fornes and Josep Llados. 2017. Towards the Alignment of Handwritten Music Scores. In Bart Lamiroy and R Dueire Lins, eds. International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges.103–116. (LNCS.)
Abstract: It is very common to nd dierent versions of the same music work in archives of Opera Theaters. These dierences correspond to modications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such dierences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
Keywords: Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
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Lluis Pere de las Heras, Oriol Ramos Terrades and Josep Llados. 2017. Ontology-Based Understanding of Architectural Drawings. International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges.75–85. (LNCS.)
Abstract: In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.
Keywords: Graphics recognition; Floor plan analysi; Domain ontology
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Marçal Rusiñol, Dimosthenis Karatzas and Josep Llados. 2015. Automatic Verification of Properly Signed Multi-page Document Images. Proceedings of the Eleventh International Symposium on Visual Computing.327–336. (LNCS 9475.)
Abstract: In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.
Keywords: Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow
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