Research On The Theory And Techniques Of Self-organizational Intelligent Recognition Of Scanned Engineering Drawings Based On Primitive Regions Adjacency Graph

Recognition of scanned engineering drawings is a comprehensive application that relates to multi-discipline, such as image processing, pattern recognition, and artificial intelligence, etc. The research on recognition is important in theory and practical application. The recognition is one of key issues in the field of CAD. Many progress in the area have been made, raster images can be partly transformed into vector data that can be used in a CAD system. However, many current approaches are limited on capturing local features and process sequentially, and the results recognized are not satisfactory, new approaches of recognition still have to be researched. An engineering drawing is a set of associated entities, which are line-like, and a scanned engineering drawing is composed of pixels. To extract entities from pixels, recognition process should be self-organizationally implemented by levels. The approach developed by author aims to capture more global features and intelligently infer with combining lower and higher local infonnation. The recognition approach places more emphasis on association relations among features. The data and knowledge are chosen and organized according to contexts of data. The recognition is to transform pixels into primitive regions. The features in the same level are associated each other. The features in different levels are also hierarchically associated each other. The recognition is to?infer self- organizationally using those associations. Scanned engineering drawings are self- organizationally recognized based on Primitive Region Adjacency Graph (PRAG). Author uses fuzzy classification, genetic algorithm and object-oriented knowledge representing in developing algorithms. The steps of processing is as follows: (1) To extract stripe features from an image, a PRAG is used to represent geometrical and topological data. It represents structure features of characters and graphics, and provides primary data for later processing. (2) Characters and their strokes, and integrated graphic primitives are extracted form the PRAG. Then, vectors recognized are stored in a vector adjacency graph (VAG). (3) Based on the VAG, constraint knowledge among vectors is organized using object-oriented knowledge representing. Entities are extracted, and associations among them are also extracted. The drawings are represented using an entity adjacency graph (EAG). Primitive regions, vectors and entities are extracted using association of features. In the same level, the global structure is constructed with local features Ill extracted. Then, it is to guild to capture local features. The recognition uses association of different levels. Knowlcdge of recognition is chosen automatically according to the start data, and some thresholds are adjusted during inferring to adapt to context changing. The associations depend on and affect each other. The association relations should be self-organized during the recognition implementation. Components of graphics and texts in drawings can be viewed as stripe regions. Recognition of images is to get stripes and their relationships. A new primitive region structure and a adjacency graph are developed for representing a scanned drawing. A primitive region can represent a line, an arc, an arrow or an intersection block, which enlarge the scope of quadrangle-like regions. A binary image is represented using a mn-length adjacency g...