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Veronica Romero, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2016. Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books. 15th international conference on Frontiers in Handwriting Recognition.
Abstract: Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous
works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.
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Lasse Martensson, Anders Hast and Alicia Fornes. 2017. Word Spotting as a Tool for Scribal Attribution. 2nd Conference of the association of Digital Humanities in the Nordic Countries.87–89.
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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2016. Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary. Recent Trends in Image Processing and Pattern Recognition.
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Arka Ujjal Dey, Suman Ghosh and Ernest Valveny. 2018. Don't only Feel Read: Using Scene text to understand advertisements. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.
Abstract: We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks.
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Antonio Lopez, Atsushi Imiya, Tomas Pajdla and Jose Manuel Alvarez. Computer Vision in Vehicle Technology: Land, Sea & Air.
Abstract: A unified view of the use of computer vision technology for different types of vehicles
Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).
The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed.
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Alicia Fornes and 6 others. 2017. ICDAR2017 Competition on Information Extraction in Historical Handwritten Records. 14th International Conference on Document Analysis and Recognition.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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Pau Riba, Anjan Dutta, Josep Llados, Alicia Fornes and Sounak Dey. 2017. Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms. 14th International Conference on Document Analysis and Recognition.475–480.
Abstract: Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the
state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario
Keywords: document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion
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Anjan Dutta, Pau Riba, Josep Llados and Alicia Fornes. 2017. Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification. 14th International Conference on Document Analysis and Recognition.33–38.
Abstract: Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).
Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support
vector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols
Keywords: graph embedding; hierarchical graph representation; graph clustering; stochastic graphlet embedding; graph classification
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Juan Ignacio Toledo, Sounak Dey, Alicia Fornes and Josep Llados. 2017. Handwriting Recognition by Attribute embedding and Recurrent Neural Networks. 14th International Conference on Document Analysis and Recognition.1038–1043.
Abstract: Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model
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Sounak Dey, Anjan Dutta, Josep Llados, Alicia Fornes and Umapada Pal. 2017. Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario. 12th IAPR International Workshop on Graphics Recognition.31–32.
Abstract: One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
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