The graphic gift : a study of the map of Australia's visual qualities

University of Technology, Sydney. Faculty of Design, Architecture and Building.This thesis examines the shape of the map of Australia, and analyses how its inherent visual qualities contribute to its recognition and ubiquity as a symbol. It is argued that the effectiveness of the map of Australia as a widely used graphic symbol stems from the characteristics of its shape, and that its attributes relate to those fundamentals of form and composition commonly accepted by graphic designers as necessary for creating an effective design. However, there is a paucity of literature that discusses or analyses maps in general and specifically the map of Australia, from this perspective. Whilst there has been some recognition from the cartographic discipline, this has simply hinted at the potential of shape as a contributing element in the recognition of maps as symbols. This literature has also been undertaken from a cartographic perspective and lacks the particular insight and understanding as to why and how the characteristics of shape can contribute to the effectiveness of a symbol. A preliminary study of the shape of the map of Australia compared to other countries of similar size or shape, initially identifies how the visual and graphic uniqueness of cartographic maps create a recognisable and memorable form, and subsequently, how these elements can contribute to the flexibility of a map to adapt as a graphic symbol. Identifying these elements is the first step in determining whether the map’s shape can serve as an effective graphic symbol. The heart of the study builds on this understanding and examines how, by applying the principles of design, we are able to understand why the map works as a symbol. This analysis develops a cohesive set of principles, which are used as an analytical tool to evaluate the map of Australia in terms of its effectiveness as a graphic symbol. This thesis is guided by my own personal knowledge as a practising designer of many years’ standing and also draws on graphic design literature that provides an insight into the specific design principles, visual identity and creative process involved in developing a symbol. Through an understanding of these core principles it can be demonstrated how and why the map of Australia serves as an effective graphic symbol. Finally, this thesis presents a series of matrices that presents the sizeable collection of logos in the research database, and which reveals the versatility, and graphic adaptability, of the map of Australia’s unique form

Applying Hierarchical Contextual Parsing with Visual Density and Geometric Features to Typeset Formula Recognition

We demonstrate that recognition of scanned typeset mathematical expression images can be done by extracting maximum spanning trees from line of sight graphs weighted using geometric and visual density features. The approach used is hierarchical contextual parsing (HCP): Hierarchical in terms of starting with connected components and building to the symbol level using visual, spatial, and contextual features of connected components. Once connected components have been segmented into symbols, a new set of spatial, visual, and contextual features are extracted. One set of visual features is used for symbol classification, and another for parsing. The features are used in parsing to assign classifications and confidences to edges in a line of sight symbol graph. Layout trees describe expression structure in terms of spatial relations between symbols, such as horizontal, subscript, and superscript. From the weighted graph Edmonds\u27 algorithm is used to extract a maximum spanning tree. Segmentation and parsing are done without using symbol classification information, and symbol classification is done independently of expression structure recognition. The commonality between the recognition processes is the type of features they use, the visual densities. These visual densities are used for shape, spatial, and contextual information. The contextual information is shown to help in segmentation, parsing, and symbol recognition. The hierarchical contextual parsing has been implemented in the Python and Graph-based Online/Offline Recognizer for Math (Pythagor^m) system and tested on the InftyMCCDB-2 dataset. We created InftyMCCDB-2 from InftyCDB-2 as a open source dataset for scanned typeset math expression recognition. In building InftyMCCDB-2 modified formula structure representations were used to better capture the spatial positioning of symbols in the expression structures. Namely, baseline punctuation and symbol accents were moved out of horizontal baselines as their positions are not horizontally aligned with symbols on a writing line. With the transformed spatial layouts and HCP, 95.97% of expressions were parsed correctly when given symbols and 93.95% correctly parsed when requiring symbol segmentation from connected components. Overall HCP reached 90.83% expression recognition rate from connected components

Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Features and Algorithms for Visual Parsing of Handwritten Mathematical Expressions

Math expressions are an essential part of scientific documents. Handwritten math expressions recognition can benefit human-computer interaction especially in the education domain and is a critical part of document recognition and analysis. Parsing the spatial arrangement of symbols is an essential part of math expression recognition. A variety of parsing techniques have been developed during the past three decades, and fall into two groups. The first group is graph-based parsing. It selects a path or sub-graph which obeys some rule to form a possible interpretation for the given expression. The second group is grammar driven parsing. Grammars and related parameters are defined manually for different tasks. The time complexity of these two groups parsing is high, and they often impose some strict constraints to reduce the computation. The aim of this thesis is working towards building a straightforward and effective parser with as few constraints as possible. First, we propose using a line of sight graph for representing the layout of strokes and symbols in math expressions. It achieves higher F-score than other graph representations and reduces search space for parsing. Second, we modify the shape context feature with Parzen window density estimation. This feature set works well for symbol segmentation, symbol classification and symbol layout analysis. We get a higher symbol segmentation F-score than other systems on CROHME 2014 dataset. Finally, we develop a Maximum Spanning Tree (MST) based parser using Edmonds\u27 algorithm, which extracts an MST from the directed line of sight graph in two passes: first symbols are segmented, and then symbols and spatial relationship are labeled. The time complexity of our MST-based parsing is lower than the time complexity of CYK parsing with context-free grammars. Also, our MST-based parsing obtains higher structure rate and expression rate than CYK parsing when symbol segmentation is accurate. Correct structure means we get the structure of the symbol layout tree correct, even though the label of the edge in the symbol layout tree might be wrong. The performance of our math expression recognition system with MST-based parsing is competitive on CROHME 2012 and 2014 datasets. For future work, how to incorporate symbol classifier result and correct segmentation error in MST-based parsing needs more research

AUTOMATIC RECOGNITION OF TRAFFIC SIGNS USING FANN AND OPEN CV

Automation Recognition of Traffic Signs is integrated and automation software for Traffic Symbol Recognition. The proposed system detects candidate regions as Maximally Stable Extremely Region (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on Artificial Neural Network (ANN) classifiers. The training data are generated from real footage road signs which will be fetched using camera board and by applying threshold values we get proper training data for each frame. By applying thinning mechanism like erode and corrode and segmentation we can recognize proper shape and symbol. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 10 frames per second, and recognizes all classes of ideogram-based (non-text) traffic symbols from real footage road signs. Comprehensive comparative results to illustrate the performance of the system are presented. https://journalnx.com/journal-article/2015023

Integrating Vocabulary Clustering with Spatial Relations for Symbol Recognition

International audienceThis paper develops a structural symbol recognition method with integrated statistical features. It applies spatial organization descriptors to the identified shape features within a fixed visual vocabulary that compose a symbol. It builds an attributed relational graph expressing the spatial relations between those visual vocabulary elements. In order to adapt the chosen vocabulary features to multiple and possible specialized contexts, we study the pertinence of unsupervised clustering to capture significant shape variations within a vocabulary class and thus refine the discriminative power of the method. This unsupervised clustering relies on cross-validation between several different cluster indices. The resulting approach is capable of determining part of the pertinent vocabulary and significantly increases recognition results with respect to the state-of-the-art. It is experimentally validated on complex electrical wiring diagram symbols

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

Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.Comment: Submitted to DAS-201