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Rahat Khan, Joost Van de Weijer, Dimosthenis Karatzas and Damien Muselet. 2013. Towards multispectral data acquisition with hand-held devices. 20th IEEE International Conference on Image Processing.2053–2057.
Abstract: We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic.
Keywords: Multispectral; mobile devices; color measurements
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Francesco Brughi, Debora Gil, Llorenç Badiella, Eva Jove Casabella and Oriol Ramos Terrades. 2014. Exploring the impact of inter-query variability on the performance of retrieval systems. 11th International Conference on Image Analysis and Recognition. Springer International Publishing, 413–420. (LNCS.)
Abstract: This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.
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Sergio Escalera, Alicia Fornes, Oriol Pujol and Petia Radeva. 2009. Multi-class Binary Symbol Classification with Circular Blurred Shape Models. 15th International Conference on Image Analysis and Processing. Springer Berlin Heidelberg, 1005–1014. (LNCS.)
Abstract: Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements.
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L.Tarazon and 6 others. 2009. Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text. 15th International Conference on Image Analysis and Processing. Springer Berlin Heidelberg, 567–574. (LNCS.)
Abstract: An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Morphology Based Handwritten Line Segmentation using Foreground and Background Information. International Conference on Frontiers in Handwriting Recognition,.241–246.
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Alicia Fornes and Josep Llados. 2010. A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores. 12th International Conference on Frontiers in Handwriting Recognition.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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Jon Almazan, David Fernandez, Alicia Fornes, Josep Llados and Ernest Valveny. 2012. A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection. 13th International Conference on Frontiers in Handwriting Recognition.453–458.
Abstract: In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.
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Marçal Rusiñol and Josep Llados. 2012. The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback. 13th International Conference on Frontiers in Handwriting Recognition.55–60.
Abstract: In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.
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Volkmar Frinken, Markus Baumgartner, Andreas Fischer and Horst Bunke. 2012. Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting. 13th International Conference on Frontiers in Handwriting Recognition.49–54.
Abstract: State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.
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Emanuel Indermühle, Volkmar Frinken and Horst Bunke. 2012. Mode Detection in Online Handwritten Documents using BLSTM Neural Networks. 13th International Conference on Frontiers in Handwriting Recognition.302–307.
Abstract: Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.
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