Abstract
This paper presents a framework named “Classifier Molding” that imitates arbitrary classifiers by linear regression trees so as to accelerate classification speed. This framework requires an accurate (but slow) classifier and large amount of training data. As an example of accurate classifier, we used the Compound Similarity Method (CSM) for Industrial Ink Jet Printer (IIJP) character recognition problem. The input-output relationship of trained CSM is imitated by a linear regression tree by providing a large amount of training data. For generating the training data, we developed a character pattern fluctuation method simulating the IIJP printing process. The learnt linear regression tree can be used as an accelerated classifier. Based on this classifier, we also developed Classification based Character Segmentation (CCS) method, which extracts character patterns from an image so as to maximize the total classification scores. Through extensive experiments, we confirmed that imitated classifiers are 1500 times faster than the original classifier without dropping the recognition rate and CCS method greatly corrects the segmentation errors of bottom-up segmentation method.
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Ota, T., Wada, T., Nakamura, T. (2011). Classifier Acceleration by Imitation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_52
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DOI: https://doi.org/10.1007/978-3-642-19282-1_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
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