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Pau Riba, Andreas Fischer, Josep Llados and Alicia Fornes. 2018. Learning Graph Distances with Message Passing Neural Networks. 24th International Conference on Pattern Recognition.2239–2244.
Abstract: Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply
these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with
(approximate) graph edit distance benchmarks.
Keywords: ★Best Paper Award★
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Jialuo Chen, Pau Riba, Alicia Fornes, Juan Mas, Josep Llados and Joana Maria Pujadas-Mora. 2018. Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts. 16th International Conference on Frontiers in Handwriting Recognition.528–533.
Abstract: Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance.
Keywords: Crowdsourcing; Gamification; Handwritten documents; Performance evaluation
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Manuel Carbonell, Mauricio Villegas, Alicia Fornes and Josep Llados. 2018. Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model. 13th IAPR International Workshop on Document Analysis Systems.399–404.
Abstract: When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
Keywords: Named entity recognition; Handwritten Text Recognition; neural networks
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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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Katerine Diaz, Jesus Martinez del Rincon, Marçal Rusiñol and Aura Hernandez-Sabate. 2019. Feature Extraction by Using Dual-Generalized Discriminative Common Vectors. JMIV, 61(3), 331–351.
Abstract: In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.
Keywords: Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning
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Raul Gomez, Lluis Gomez, Jaume Gibert and Dimosthenis Karatzas. 2018. Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods. 15th European Conference on Computer Vision Workshops.530–544. (LNCS.)
Abstract: Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis.
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Y. Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas and C.V. Jawahar. 2018. TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces.
Abstract: The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN.
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Anguelos Nicolaou, Sounak Dey, V.Christlein, A.Maier and Dimosthenis Karatzas. 2018. Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings. International Workshop on Reproducible Research in Pattern Recognition.71–82. (LNCS.)
Abstract: Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
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Dena Bazazian, Dimosthenis Karatzas and Andrew Bagdanov. 2018. Word Spotting in Scene Images based on Character Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.1872–1874.
Abstract: In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
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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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