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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2023 / Cilt: 11 Sayı: 3

Recognition of Online Turkish Handwriting using Transfer Learning

Esma Fatıma BİLGİN TAŞDEMİR
Pages
719–726
Publication Date
2023-09-27
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that transfer learning can be a solution to the data scarcity problem of online Turkish handwriting.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We present a recognition system for online Turkish handwriting using transfer learning.
📊
Key Findings & Data: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%.
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Conclusion & Impact:The results show that transfer learning can be a solution to the data scarcity problem of online Turkish handwriting.
🔬Design:Research Article
📍Scope:2,041 samples
⚙️Variables:Online Handwriting Recognition · Deep Learning · Transfer Learning

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.

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Cite This Article

TAŞDEMİR, E. F. B. (2023). Recognition of Online Turkish Handwriting using Transfer Learning. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 11 Sayı: 3, 719–726. https://ajindex.com/articles/962815

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