Alicia Fornes, & Josep Llados. (2010). A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores. In 12th International Conference on Frontiers in Handwriting Recognition (pp. 634–639).
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we introduce a symbol-dependent approach for identifying the writer of old music scores, which is based on two symbol recognition methods. The main idea is to use the Blurred Shape Model descriptor and a DTW-based method for detecting, recognizing and describing the music clefs and notes. The proposed approach has been evaluated in a database of old music scores, achieving very high writer identification rates.
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Alicia Fornes, Josep Llados, & Gemma Sanchez. (2007). Old Handwritten Musical Symbol Classification by a Dynamic Time Warping Based Method. In Seventh IAPR International Workshop on Graphics Recognition (26–27).
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Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2009). On the use of textural features for writer identification in old handwritten music scores. In 10th International Conference on Document Analysis and Recognition (pp. 996–1000).
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.
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Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2009). Symbol-independent writer identification in old handwritten music scores. In In proceedings of 8th IAPR International Workshop on Graphics Recognition (186–197). Springer Berlin Heidelberg.
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Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2008). Writer Identification in Old Handwritten Music Scores. In Proceedings of the 8th International Workshop on Document Analysis Systems, (347–353).
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Alicia Fornes, Josep Llados, Joan Mas, Joana Maria Pujadas-Mora, & Anna Cabre. (2014). A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts. In Digital Access to Textual Cultural Heritage Conference (pp. 103–108).
Abstract: In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts.
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Alicia Fornes, Sergio Escalera, Josep Llados, & Ernest Valveny. (2010). Symbol Classification using Dynamic Aligned Shape Descriptor. In 20th International Conference on Pattern Recognition (1957–1960).
Abstract: Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates.
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Alicia Fornes, Sergio Escalera, Josep Llados, & Gemma Sanchez. (2007). Symbol Recognition by Multi-class Blurred Shape Models. In Seventh IAPR International Workshop on Graphics Recognition (11–13).
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Alicia Fornes, Veronica Romero, Arnau Baro, Juan Ignacio Toledo, Joan Andreu Sanchez, Enrique Vidal, et al. (2017). ICDAR2017 Competition on Information Extraction in Historical Handwritten Records. In 14th International Conference on Document Analysis and Recognition (pp. 1389–1394).
Abstract: The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results.
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Alicia Fornes, Volkmar Frinken, Andreas Fischer, Jon Almazan, G. Jackson, & Horst Bunke. (2011). A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors. In Proceedings of the 2011 Workshop on Historical Document Imaging and Processing (pp. 83–90). ACM.
Abstract: The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
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Alicia Fornes, Xavier Otazu, & Josep Llados. (2013). Show through cancellation and image enhancement by multiresolution contrast processing. In 12th International Conference on Document Analysis and Recognition (pp. 200–204).
Abstract: Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities.
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Alloy Das, Sanket Biswas, Ayan Banerjee, Josep Llados, Umapada Pal, & Saumik Bhattacharya. (2024). Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance. In Winter Conference on Applications of Computer Vision (pp. 718–728).
Abstract: The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency.
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Alloy Das, Sanket Biswas, Umapada Pal, & Josep Llados. (2024). Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes. In IEEE International Conference on Robotics and Automation in PACIFICO.
Abstract: When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance.
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Alvaro Cepero, Albert Clapes, & Sergio Escalera. (2013). Quantitative analysis of non-verbal communication for competence analysis. In 16th Catalan Conference on Artificial Intelligence (Vol. 256, pp. 105–114).
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Alvaro Peris, Marc Bolaños, Petia Radeva, & Francisco Casacuberta. (2016). Video Description Using Bidirectional Recurrent Neural Networks. In 25th International Conference on Artificial Neural Networks (Vol. 2, pp. 3–11).
Abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Keywords: Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks
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