Abstract
We propose to use novel and classical audio and text signal-processing and otherwise techniques for “inexpensive” fast writer identification tasks of scanned hand-written documents “visually”. The “inexpensive” refers to the efficiency of the identification process in terms of CPU cycles while preserving decent accuracy for preliminary identification. This is a comparative study of multiple algorithm combinations in a pattern recognition pipeline implemented in Java around an open-source Modular Audio Recognition Framework (MARF) that can do a lot more beyond audio. We present our preliminary experimental findings in such an identification task. We simulate “visual” identification by “looking” at the hand-written document as a whole rather than trying to extract fine-grained features out of it prior classification.
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Acknowledgments
This work is partially funded by NSERC, FQRSC, and Graduate School and the Faculty of Engineering and Computer Science, Concordia University, Montreal, Canada.
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Mokhov, S.A., Song, M., Suen, C.Y. (2010). Writer Identification Using Inexpensive Signal Processing Techniques. In: Sobh, T., Elleithy, K. (eds) Innovations in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9112-3_74
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DOI: https://doi.org/10.1007/978-90-481-9112-3_74
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