Abstract
One avenue to improve the performance of computer systems for the recognition of handwritten data is to develop a better grasp of human expertise in this area. Through an experiment, we investigate the clues and cues used by humans to recognize the most confusing samples of our database. Four algorithms are also applied, individually and in a combined manner, to the same difficult data set. Machine and human performances are compared. Such knowledge should be used to refine the existing algorithms which are proved to be clearly inferior to the human experts in recognition of unconstrained handwritten numerals.
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© 1992 Springer-Verlag Berlin Heidelberg
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Legault, R., Suen, C.Y., Nadal, C. (1992). Difficult Cases in Handwritten Numeral Recognition. In: Baird, H.S., Bunke, H., Yamamoto, K. (eds) Structured Document Image Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77281-8_11
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DOI: https://doi.org/10.1007/978-3-642-77281-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77283-2
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