Manuel Carbonell, Joan Mas, Mauricio Villegas, Alicia Fornes, & Josep Llados. (2019). End-to-End Handwritten Text Detection and Transcription in Full Pages. In 2nd International Workshop on Machine Learning (Vol. 5, pp. 29–34).
Abstract: When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
Keywords: Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning
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Asma Bensalah, Pau Riba, Alicia Fornes, & Josep Llados. (2019). Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning. In 13th IAPR International Workshop on Graphics Recognition (pp. 80–85).
Abstract: With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
Keywords: Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
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Pau Riba, Anjan Dutta, Lutz Goldmann, Alicia Fornes, Oriol Ramos Terrades, & Josep Llados. (2019). Table Detection in Invoice Documents by Graph Neural Networks. In 15th International Conference on Document Analysis and Recognition (pp. 122–127).
Abstract: Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
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Ekta Vats, Anders Hast, & Alicia Fornes. (2019). Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion. In 15th International Conference on Document Analysis and Recognition (pp. 1294–1299).
Abstract: Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
Keywords: Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching
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Bogdan Raducanu, Jordi Vitria, & D. Gatica-Perez. (2009). You are Fired! Nonverbal Role Analysis in Competitive Meetings. In IEEE International Conference on Audio, Speech and Signal Processing (1949–1952).
Abstract: This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
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Marc Oliu, Ciprian Corneanu, Laszlo A. Jeni, Jeffrey F. Cohn, Takeo Kanade, & Sergio Escalera. (2016). Continuous Supervised Descent Method for Facial Landmark Localisation. In 13th Asian Conference on Computer Vision (Vol. 10112, pp. 121–135). LNCS.
Abstract: Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.
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Emanuel Sanchez Aimar, Petia Radeva, & Mariella Dimiccoli. (2019). Social Relation Recognition in Egocentric Photostreams. In 26th International Conference on Image Processing (pp. 3227–3231).
Abstract: This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.
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Ariel Amato, Mikhail Mozerov, Ivan Huerta, Jordi Gonzalez, & Juan J. Villanueva. (2008). ackground Subtraction Technique Based on Chromaticity and Intensity Patterns. In 19th International Conference on Pattern Recognition, (1–4).
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Murad Al Haj, Francisco Javier Orozco, Jordi Gonzalez, & Juan J. Villanueva. (2008). Automatic Face and Facial Features Initialization for Robust and Accurate Tracking. In 19th International Conference on Pattern Recognition. (1– 4).
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Partha Pratim Roy, Umapada Pal, Josep Llados, & F. Kimura. (2008). Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents. In 19th International Conference on Pattern Recognition.
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H. Chouaib, Oriol Ramos Terrades, Salvatore Tabbone, F. Cloppet, & N. Vincent. (2008). Feature Selection Combining Genetic Algorithm and Adaboost Classifiers. In 19th International Conference on Pattern Recognition (pp. 1–4).
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Salvatore Tabbone, Oriol Ramos Terrades, & S. Barrat. (2008). Histogram of radon transform. A useful descriptor for shape retrieval. In 19th International Conference on Pattern Recognition (pp. 1–4).
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Miquel Ferrer, Ernest Valveny, F. Serratosa, K. Riesen, & Horst Bunke. (2008). An Approximate Algorith for Median Graph Computation using Graph Embedding. In 19th International Conference on Pattern Recognition..
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Dimosthenis Karatzas, Marçal Rusiñol, Coen Antens, & Miquel Ferrer. (2008). Segmentation Robust to the Vignette Effect for Machine Vision Systems. In 19th International Conference on Pattern Recognition.
Abstract: The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
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Jose Antonio Rodriguez, Florent Perronnin, Gemma Sanchez, & Josep Llados. (2008). Unsupervised writer style adaptation for handwritten word spotting. In Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award..
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