@article{article_1039846, title={Online Turkish Handwriting Recognition Using Synthetic Data}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={649–656}, year={2021}, DOI={10.31590/ejosat.1039846}, author={Bilgin Taşdemir, Esma Fatıma}, keywords={Turkish Handwriting Recognition, Online Handwriting Recognition, Deep Learning, Synthetic Data Generation, Transfer Learning.}, abstract={We present a recognition system for online Turkish handwriting trained with synthetically generated data and transfer learning. Training deep networks requires large amounts of data. However, a sufficiently large collection of Turkish handwriting samples is not available. Hence we synthesize data to do pretraining before adapting the system to target dataset by fine tuning. We generate words from isolated character collection of a large English handwriting dataset. Then, we train the system first with synthetic data and fine tune it with Turkish handwriting samples from a smaller dataset. Fine tuning increases the character recognition rate of the final system which is evaluated on 2,041 samples of isolated Turkish words from the initial value of 61% to 88%. Performance of the system on synthetic data is quite similar to that on the Turkish test data which shows that the synthetic data resembles the real data quite closely. According to these results, synthetic data generation can be a solution to the data scarcity problem of online Turkish handwriting.}, number={32}, publisher={Osman SAĞDIÇ}