Abstract
Three different methods for the synthetic generation of handwritten text are introduced. These methods are experimentally evaluated in the context of a cursive handwriting recognition task, using an HMM-based recognizer. In the experiments, the performance of a traditional recognizer, which is trained on data produced by human writers, is compared to a system that is trained on synthetic data only. Under the most elaborate synthetic handwriting generation model, a level of performance comparable to, or even slightly better than, the system trained on the writing of humans was observed.
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Helmers, M., Bunke, H. (2003). Generation and Use of Synthetic Training Data in Cursive Handwriting Recognition. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_39
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DOI: https://doi.org/10.1007/978-3-540-44871-6_39
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