Applying algebraic and differential invariants for logo recognition
Machine Vision and Applications
Combination of Invariant Pattern Recognition Primitives on Technical Documents
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Sketch-Based User Interface for Inputting Graphic Objects on Small Screen Devices
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Online parsing of visual languages using adjacency grammars
VL '95 Proceedings of the 11th International IEEE Symposium on Visual Languages
Automatic construction of user interfaces from constraint multiset grammars
VL '95 Proceedings of the 11th International IEEE Symposium on Visual Languages
An adjacency grammar to recognize symbols and gestures in a digital pen framework
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
An efficient and effective similarity measure to enable data mining of petroglyphs
Data Mining and Knowledge Discovery
Searching historical manuscripts for near-duplicate figures
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Hi-index | 0.00 |
Syntactic approaches on structural symbol recognition are characterized by defining symbols using a grammar. Following the grammar productions a parser is constructed to recognize symbols: given an input, the parser detects whether it belongs to the language generated by the grammar, recognizing the symbol, or not. In this paper, we describe a parsing methodology to recognize a set of symbols represented by an adjacency grammar. An adjacency grammar is a grammar that describes a symbol in terms of the primitives that form it and the relations among these primitives. These relations are called constraints, which are validated using a defined cost function. The cost function approximates the distortion degree associated to the constraint. When a symbol has been recognized the cost associated to the symbol is like a similarity value. The evaluation of the method has been realized from a qualitative point of view, asking some users to draw some sketches. From a quantitative point of view a benchmarking database of sketched symbols has been used.