Generation and Use of Synthetic Training Data in Cursive Handwriting Recognition

摘要

Three different methods for the synthetic generation of hand-written 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.