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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez;Josep Llados |
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Title |
Perceptual retrieval of architectural floor plans |
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Conference Article |
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Year |
2013 |
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10th IAPR International Workshop on Graphics Recognition |
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This paper proposes a runlength histogram signature as a percetual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query,
similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Preliminary results show the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.056; 600.061 |
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no |
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Admin @ si @ HFF2013a |
Serial |
2320 |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Combining structural and statistical strategies for unsupervised wall detection in floor plans |
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Conference Article |
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Year |
2013 |
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10th IAPR International Workshop on Graphics Recognition |
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This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated data
to learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing the
process in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on
4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045 |
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Admin @ si @ HVS2013a |
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2321 |
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Dimosthenis Karatzas; Faisal Shafait; Seiichi Uchida; Masakazu Iwamura; Lluis Gomez; Sergi Robles; Joan Mas; David Fernandez; Jon Almazan; Lluis Pere de las Heras |
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Title |
ICDAR 2013 Robust Reading Competition |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1484-1493 |
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This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.056 |
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Call Number |
Admin @ si @ KSU2013 |
Serial |
2318 |
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Author |
Lluis Gomez |
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Title |
Perceptual Organization for Text Extraction in Natural Scenes |
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Report |
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Year |
2012 |
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CVC Technical Report |
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173 |
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Bellaterra |
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Master's thesis |
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DAG |
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no |
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Admin @ si @ Gom2012 |
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2309 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-script Text Extraction from Natural Scenes |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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467-471 |
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Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.056; 601.158; 601.197 |
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no |
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Call Number |
Admin @ si @ GoK2013 |
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2310 |
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Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Writer identification in handwritten musical scores with bags of notes |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
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5 |
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1337-1345 |
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Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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0031-3203 |
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DAG |
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no |
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Call Number |
Admin @ si @ GFV2013 |
Serial |
2307 |
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Author |
David Fernandez; Simone Marinai; Josep Llados; Alicia Fornes |
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Title |
Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks |
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Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Historical Document Imaging and Processing |
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36-43 |
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Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information. |
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washington; USA; August 2013 |
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978-1-4503-2115-0 |
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HIP |
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DAG; 600.056; 600.045; 600.061; 602.006 |
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Admin @ si @ FML2013 |
Serial |
2308 |
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Author |
Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
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Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
Issue |
3 |
Pages |
1073-1082 |
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Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
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The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. |
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DAG; 600.077; 602.101 |
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Admin @ si @ FFB2014 |
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2297 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; Tomokazu Sato; Masakazu Iwamura; Koichi Kise |
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Title |
Key-region detection for document images -applications to administrative document retrieval |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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230-234 |
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In this paper we argue that a key-region detector designed to take into account the special characteristics of document images can result in the detection of less and more meaningful key-regions. We propose a fast key-region detector able to capture aspects of the structural information of the document, and demonstrate its efficiency by comparing against standard detectors in an administrative document retrieval scenario. We show that using the proposed detector results to a smaller number of detected key-regions and higher performance without any drop in speed compared to standard state of the art detectors. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.056; 600.045 |
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Admin @ si @ GRK2013b |
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2293 |
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Author |
Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen |
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Title |
Improving HMM-Based Keyword Spotting with Character Language Models |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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506-510 |
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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 605.203 |
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no |
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Call Number |
Admin @ si @ FFB2013 |
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2295 |
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