Engineering Drawing Database Retrieval Using Statistical Pattern Spotting Techniques
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
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A new approach to identification of handwritten symbols in arbitrary complex environments is presented. 20 different pictograms drawn in different backgrounds can be identified with a recognition accuracy of 90%. In order to perform this challenging task, we use pattern spotting techniques based on pseudo 2-D hidden Markov models (P2DHMMs). Practical applications of our approach can be found in many typical multimedia document processing tasks, such as localization and recognition of non-rigid objects in image databases, detection of objects in complex scenes, finding trademarks in presence of clutter within videos, processing distorted document images in digital libraries, or content-based image retrieval based on handwritten query symbols.