Yushun Liu
ABSTRACT
This paper examines the performance of a specific Long-Short Term Memory Recurrent Neural Network for cursive handwriting generation when training with small datasets. The RNN can generate complex structure sequences by predicting one data point at a time. Then, by predicting the overall writing structure, the handwriting can be synthesized. The resulting network can generate different handwriting style parameters.
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Index Terms
- Experiment on Handwriting Generation with Recurrent Neural Networks using Small Datasets
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