Symbol Recognition using Spatial Relations

International audienceIn this paper, we present a method for symbol recognition based on the spatio-structural description of a 'vocabulary' of extracted visual elementary parts. It is applied to symbols in electrical wiring diagrams. The method consists of first identifying vocabulary elements into different groups based on their types (e.g., circle, corner ). We then compute spatial relations between the possible pairs of labelled vocabulary types which are further used as a basis for building an Attributed Relational Graph that fully describes the symbol. These spatial relations integrate both topology and directional information. The experiments reported in this paper show that this approach, used for recognition, significantly outperforms both structural and signal-based state-of-the-art methods

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

BoR: Bag-of-Relations for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in bags-of-relations and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications

Probabilistic mathematical formula recognition using a 2D context-free graph grammar

We present a probabilistic framework for the mathematical expression recognition problem. The developed system is flexible in that its grammar can be extended easily thanks to its graph grammar which eliminates the need for specifying rule precedence. It is also optimal in the sense that all possible interpretations of the expressions are expanded without making early commitments or hard decisions. In this paper, we give an overview of the whole system and describe in detail the graph grammar and the parsing process used in the system, along with some preliminary results on character, structure and expression recognition performances

Intelligent computational sketching support for conceptual design

Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Efficient Analysis of Complex Diagrams using Constraint-Based Parsing

This paper describes substantial advances in the analysis (parsing) of diagrams using constraint grammars. The addition of set types to the grammar and spatial indexing of the data make it possible to efficiently parse real diagrams of substantial complexity. The system is probably the first to demonstrate efficient diagram parsing using grammars that easily be retargeted to other domains. The work assumes that the diagrams are available as a flat collection of graphics primitives: lines, polygons, circles, Bezier curves and text. This is appropriate for future electronic documents or for vectorized diagrams converted from scanned images. The classes of diagrams that we have analyzed include x,y data graphs and genetic diagrams drawn from the biological literature, as well as finite state automata diagrams (states and arcs). As an example, parsing a four-part data graph composed of 133 primitives required 35 sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at ftp://ftp.ccs.neu.edu/pub/people/futrell