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Segmentation and classification of hand-drawn pictogram in cluttered scenes-an integrated approach

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4 Author(s)
S. Muller ; Dept. of Comput. Sci., Gerhard-Mercator-Univ., Duisburg, Germany ; S. Eickeler ; C. Neukirchen ; B. Winterstein

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

Published in:

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on  (Volume:6 )

Date of Conference:

15-19 Mar 1999