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Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez |
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Title |
Statistical Segmentation and Structural Recognition for Floor Plan Interpretation |
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2014 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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17 |
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3 |
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221-237 |
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A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.076; 600.077 |
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HSL2014 |
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2370 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke |
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Title |
A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores |
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Journal Article |
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2010 |
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International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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13 |
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4 |
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243-259 |
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The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers. |
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Springer-Verlag |
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1433-2833 |
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DAG; CAT;CIC |
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FLS2010b |
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1319 |
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Ernest Valveny; Philippe Dosch; Adam Winstanley; Yu Zhou; Su Yang; Luo Yan; Liu Wenyin; Dave Elliman; Mathieu Nicolas Delalandre; Eric Trupin; Sebastien Adam; Jean-Marc Ogier |
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A general framework for the evaluation of symbol recognition methods |
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2006 |
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International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74 |
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DAG @ dag @ VDW2006 |
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801 |
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Ernest Valveny; Philippe Dosch |
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Title |
A General Framework for the Evaluation of Symbol Recognition Methods |
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2007 |
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International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74 |
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DAG @ dag @ VaD2007 |
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893 |
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Ernest Valveny; Philippe Dosch |
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A general framework for the evaluation of symbol recognition methods |
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2006 |
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International Journal on Document Analysis and Recognition |
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DAG @ dag @ VaD2006 |
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686 |
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