Flexible Structural Signature for Symbol Recognition using a Concept Lattice Classifier (original) (raw)
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On the Joint Use of a Structural Signature and a Galois Lattice Classifier for Symbol Recognition
Lecture Notes in Computer Science, 2008
In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois Lattice as classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures, which can be seen as dynamic paths which carry high level information, are robust towards various transformations. They are classified by using a Galois Lattice as a classifier. The performances of the proposed approach are evaluated on the GREC03 symbol database and the experimental results we obtain are encouraging.
Symbol Recognition Using a Concept Lattice of Graphical Patterns
Lecture Notes in Computer Science, 2010
In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest.
Robust Symbol Recognition using a Structural Approach Mathieu Delalandre CVC (Barcelona, Spain)
In this paper we present a robust system of symbol recognition using a structural approach. Our key objective here is to provide a system, equaling the statistical ones in robustness concerning the recognition, to apply next to localization. To do it we have investigated two particular structural methods: the straight line detection using Hough Transform and the vector templates matching. Experiments done on the GREC2003 database show how their combination allows to obtain high recognition results.
Robust Symbol Recognition using a Structural Approach
In this paper we present a robust system of symbol recognition using a structural approach. Our key objective here is to provide a system, equaling the statistical ones in robustness concerning the recognition, to apply next to localization. To do it we have investigated two particular structural methods: the straight line detection using Hough Transform and the vector templates matching. Experiments done on the GREC2003 database show how their combination allows to obtain high recognition results.
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
2009
We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of presegmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.
2010
Motivation of our work is to present a new methodology for symbol recognition. We support structural methods for representing visual associations in graphic documents. The proposed method employs a structural approach for symbol representation and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an ARG and compute a signature from this structural graph. To address the sensitivity of structural representations to deformations and degradations, we use data adapted fuzzy intervals while computing structural signature. The joint probability distribution of signatures is encoded by a Bayesian network. This network in fact serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures, for underlying symbol set. Finally we deploy the Bayesian network in supervised learning scenario for recognizing query symbols. We have evaluated the robustness of our method against noise, on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates. A second set of experimentation was carried out for evaluating the performance of our method against context noise i.e. symbols cropped from complete documents. The results support the use of our signature by a symbol spotting system.
Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition
2010
The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols.
Combination of Symbolic and Statistical Features for Symbols Recognition
2007 International Conference on Signal Processing, Communications and Networking, 2007
In this article, we have tried to explore a new hybrid approach which well integrates the advantages of structural and statistical approaches and avoids their weaknesses. In the proposed approach, the graphic symbols are first segmented into high-level primitive like quadrilaterals. Then, a graph is built by utilizing these quadrilaterals as nodes and their spatial relationships as edges. Additional information like relative length of the quadrilaterals and their relative angles with neighbouring quadrilaterals are associated as attributes to the nodes and edges of the graph respectively. However, the observed graphs are subject to deformations due to noise and/or vectorial distortion (in case of hand-drawn images) hence differs somewhat from their ideal models by either missing or extra nodes and edges appearance. Therefore, we propose a method that computes a measure of similarity between two given graphs instead of looking for exact isomorphism. The approach is based on comparing feature vectors extracted from the graphs. The idea is to use features that can be quickly computed from a graph on the one hand, but are, on the other hand, effective in discriminating between the various graphs in the database. The nearest neighbour rule is used as a classifier due to its simplicity and good behaviour.
Navigala: An Original Symbol Classifier Based on Navigation Through a Galois Lattice
International Journal of Pattern Recognition and Artificial Intelligence, 2011
This paper deals with a supervised classification method, using Galois Lattices based on a navigation-based strategy. Coming from the field of data mining techniques, most literature on the subject using Galois lattices relies on selection-based strategies, which consists of selecting/choosing the concepts which encode the most relevant information from the huge amount of available data. Generally, the classification step is then processed by a classical classifier such as the k-nearest neighbors rule or the Bayesian classifier. Opposed to these selection-based strategies are navigation-based approaches which perform the classification stage by navigating through the complete lattice (similar to the navigation in a classification tree), without applying any selection operation. Our approach, named Navigala, proposes an original navigation-based approach for supervised classification, applied in the context of noisy symbol recognition. Based on a state of the art dealing with Galois ...
Robust Hough-Based Symbol Recognition Using Knowledge-Based Hierarchical Neural Networks
Abstract-A robust method for symbol recognition is presented that utilizes a compact signature based on a modified Hough Transform (HT) and knowledge-based hierarchical neural network structure. Relative position and orientation information is extracted from a symbol image using a modified Hough Transform (HT). This information is transformed and compressed into a compact, 1-D signature vector that is invariant to geometric transformations such as translation, rotation, scaling, and reflection.