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This study uses natural language processing (NLP) tools to analyze how politicians recreate the stereotype of immigrants in the Spanish Parliament. An interdisciplinary approach from computational linguistics and social psychology has been used to construct a variety of indices about content and linguistic styles. The analysis of 2,516 parliamentary interventions about immigration delivered between 1996 and 2016 by representatives of the two political parties that alternated in power during that period (conservative Popular Party and Spanish Socialist Party) shows that both the rhetorical strategy to present immigrants as "victims" or as a "threat" and the language style that politicians use reveal an interaction between the ideology of the party and the party's political position in government or in the opposition. Results also suggest some changes over time in the polarization and politicization of the debate about immigration. [ABSTRACT FROM AUTHOR]

Keyword Spotting (KWS) is here considered as a basic technology for Probabilistic Indexing (PrIx) of large collections of handwritten text images to allow fast textual access to the contents of these collections. Under this perspective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing formal insights which help understanding classical statements of KWS (from which PrIx borrows fundamental concepts), as well as the relative challenges entailed by these statements. The development of the proposed framework makes it clear that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it suggests that the same statistical models and training methods successfully used for handwriting text recognition can advantageously be used also for PrIx, even though PrIx does not generally require or rely on any kind of previously produced image transcripts. Experiments carried out using these approaches support the consistency and the general interest of the proposed framework. Results on three datasets traditionally used for KWS benchmarking are significantly better than those previously published for these datasets. In addition, good results are also reported on two new, larger handwritten text image datasets (Bentham and Plantas), showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of untranscribed handwritten documents. Specifically, we achieved the following Average Precision values: IAMDB: 0.89, George Washington: 0.91, Parzival: 0.95, Bentham: 0.91 and Plantas: 0.92. [ABSTRACT FROM AUTHOR]

November The offer of online, automated, and impersonal services demand users to upload scanned copies of their documents to the organisations. As a consequence of this decentralisation, the documents present more challenges to the already complex process of image processing and information extraction. To address this problem, the authors presented an optimised fully convolutional neural network model for document segmentation that works on mobile devices to detect the region of the document in the captured image. They performed experiments in three representative datasets comparing the proposed method with the Geodesic object Proposals, U‐net, Mask R‐CNN, and OctHU‐PageScan algorithms. They also compared the proposed model with all competitors of the ICDAR2015 Competition on smartphone document capture. Furthermore, they performed a qualitative and comparative analysis with the CamScanner software, a popular app for Android and iOS smartphones used for more than 100 million users in over 200 countries. The proposed approach achieved a significant performance compared with the current state‐of‐the‐art methods, providing a powerful approach for document segmentation in photos and scanned images. [ABSTRACT FROM AUTHOR]

November The offer of online, automated, and impersonal services demand users to upload scanned copies of their documents to the organisations. As a consequence of this decentralisation, the documents present more challenges to the already complex process of image processing and information extraction. To address this problem, the authors presented an optimised fully convolutional neural network model for document segmentation that works on mobile devices to detect the region of the document in the captured image. They performed experiments in three representative datasets comparing the proposed method with the Geodesic object Proposals, U-net, Mask R-CNN, and OctHU-PageScan algorithms. They also compared the proposed model with all competitors of the ICDAR2015 Competition on smartphone document capture. Furthermore, they performed a qualitative and comparative analysis with the CamScanner software, a popular app for Android and iOS smartphones used for more than 100 million users in over 200 countries. The proposed approach achieved a significant performance compared with the current state-of-the-art methods, providing a powerful approach for document segmentation in photos and scanned images. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid approach using hidden Markov models (HMM) and artificial neural networks to deal with the task of handwritten Music Recognition in mensural notation. Previous works have shown that the task can be addressed with Gaussian density HMMs that can be trained and used in an end-to-end manner, that is, without prior segmentation of the symbols. However, the results achieved using that approach are not sufficiently accurate to be useful in practice. In this work, we hybridize HMMs with deep multilayer perceptrons (MLPs), which lead to remarkable improvements in optical symbol modeling. Moreover, this hybrid architecture maintains important advantages of HMMs such as the ability to properly model variable-length symbol sequences through segmentation-free training, and the simplicity and robustness of combining optical models with N-gram language models, which provide statistical a priori information about regularities in musical symbol concatenation observed in the training data. The results obtained with the proposed hybrid MLP-HMM approach outperform previous works by a wide margin, achieving symbol-level error rates around 26%, as compared with about 40% reported in previous works. [ABSTRACT FROM AUTHOR]

Keyword spotting techniques are becoming cost-effective solutions for information retrieval in handwritten documents. We explore the extension of the single-word, line-level probabilistic indexing approach described in our previous works to allow for page-level search of queries consisting in Boolean combinations of several single-keywords. We propose heuristic rules to combine the single-word relevance probabilities into probabilistically consistent confidence scores of the multi-word boolean combinations. An empirical study, also presented in this paper, evaluates the search performance of word-pair queries involving AND and OR Boolean operations. Results of this study support the proposed approach and clearly show its effectiveness. Finally, a web-based demonstration system based on the proposed methods is presented. [ABSTRACT FROM AUTHOR]

We present a process for cost-effective transcription of cursive handwritten text images that has been tested on a 1,000-page 17th-century book about botanical species. The process comprised two main tasks, namely: (1) preprocessing: page layout analysis, text line detection, and extraction; and (2) transcription of the extracted text line images. Both tasks were carried out with semiautomatic procedures, aimed at incrementally minimizing user correction effort, by means of computer-assisted line detection and interactive handwritten text recognition technologies. The contribution derived from this work is three-fold. First, we provide a detailed human-supervised transcription of a relatively large historical handwritten book, ready to be searchable, indexable, and accessible to cultural heritage scholars as well as the general public. Second, we have conducted the first longitudinal study to date on interactive handwriting text recognition, for which we provide a very comprehensive user assessment of the real-world performance of the technologies involved in this work. Third, as a result of this process, we have produced a detailed transcription and document layout information (i.e. high-quality labeled data) ready to be used by researchers working on automated technologies for document analysis and recognition. [ABSTRACT FROM AUTHOR]

This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copymark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but contextaware. Taken together, thiswork suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity. [ABSTRACT FROM AUTHOR]

Currently, automatic handwriting recognition systems are ineffectual in unconstrained handwriting documents. Therefore, to obtain perfect transcriptions, heavy human intervention is required to validate and correct the results of such systems. Given that this post-editing process is inefficient and uncomfortable, a multimodal interactive approach has been proposed in previous works, which aims at obtaining correct transcriptions with the minimum human effort. In this approach, the user interacts with the system by means of an e-pen and/or more traditional methods such as keyboard or mouse. This user's feedback allows to improve system accuracy and multimodality increases system ergonomics and user acceptability. Until now, multimodal interaction has been considered only at whole-word level. In this work, multimodal interaction at character-level is studied, that may lead to more effective interactivity, since it is faster and easier to write only one character rather than a whole word. Here we study this kind of fine-grained multimodal interaction and present developments that allow taking advantage of interaction-derived context to significantly improve feedback decoding accuracy. Empirical tests on three cursive handwritten tasks suggest that, despite losing the deterministic accuracy of traditional peripherals, this approach can save significant amounts of user effort with respect to fully manual transcription as well as to noninteractive post-editing correction. [ABSTRACT FROM AUTHOR]