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- W4386007987 abstract "We present a recognition system for online Turkish handwriting using transfer learning. Training deep networks requires large amounts of data. Since such a sufficiently large collection of Turkish handwriting samples is not available, So we adopt the transfer learning approach and train and optimize a CNN-BLSTM recognition system first using the standard IAM-On dataset of English handwriting. Then, we 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 49% to 85%. The results show that transfer learning can be a solution to the data scarcity problem of online Turkish handwriting." @default.
- W4386007987 created "2023-08-20" @default.
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- W4386007987 date "2023-09-27" @default.
- W4386007987 modified "2023-10-09" @default.
- W4386007987 title "Recognition of Online Turkish Handwriting using Transfer Learning" @default.
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- W4386007987 doi "https://doi.org/10.29109/gujsc.1141508" @default.
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