Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

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 pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-132

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.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

Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition

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.Comment: 10 pages, Eighth IAPR International Workshop on Graphics RECognition (GREC), 2009, volume 8, 22-3

Brand Marks' performance in digital media

The aim of this paper is to evaluate the graphic resistance and visual performance of Brand Marks in use on websites and social media. It aims to bring knowledge about the impact of digital media on the design of contemporary Brand Marks, but especially the limitations observed in brand trademarks originated during the 20th century or previously. Considering nowadays impact of online and digital communication, the internet of things, and the diversity of multiple screen dimensions, it is important to take a closer look at the performance of Brand Marks on websites, responsive web pages, audio-visuals, and social media. This topic is very relevant when studying or developing flexible systems of brand identification or even Brand Mark variants and respective visual guidelines. Specifically, we intend to observe how the design of Brand Marks and the digital environment compromise the graphic coherence of Visual Identity and brand identification. A systematic methodology was adopted, with a non-interventionist base, with the case study of 32 large and international brands. The results consist in the identification of a set of principles and graphic features which Brand Marks should follow to ensure its recognition, the coherence of Visual Identity and brand identification.info:eu-repo/semantics/publishedVersio

Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures

Bayesian Network, Ahmed Rebai (Ed.), ISBN: 978-953-307-124-4, pp. 13-38

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content