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

Recognition of Online Turkish Handwriting using Transfer Learning

Sayfa
719–726
Yayın Tarihi
2023-09-27
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:We present a recognition system for online Turkish handwriting using transfer learning.
📊
Temel Bulgular & Veri: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%.
💡
Sonuç & Katkı:The results show that transfer learning can be a solution to the data scarcity problem of online Turkish handwriting.
🔬Tür:Araştırma Makalesi
📍Kapsam:2,041 samples
⚙️Değişkenler: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.

🔖

Bu Makaleye Atıf Yap

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

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. R. Plamondon and S. N. Srihari, “On-line and off-line handwriting recognition: A comprehensive survey”, IEEE Trans. Pattern Anal. Mach.Intell., vol. 22, no. 1, pp. 63–84, 2000
  2. B. M. Al-Helali and S. A. Mahmoud, “Arabic online handwriting recognition (AOHR): A survey”, ACM Comput. Surv., vol. 50, no. 3, pp. 33:1–33:35, 2017
  3. N. Tagougui, M. Kherallah, and A. M. Alimi, “Online Arabic handwriting recognition: a survey”, IJDAR, vol. 16, no. 3, pp. 209–226, 2013
  4. D. S. Doermann and S. Jaeger, Eds., Arabic and Chinese handwriting recognition - SACH 2006 Summit College Park, MD, USA, September 27-28, 2006 Selected Papers, ser. Lecture Notes in Computer Science, vol. 4768. Springer, 2008
  5. A. Priya, S. Mishra, S. Raj, S. Mandal, and S. Datta, “Online and offline character recognition: A survey”, in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 0967–0970
  6. M. Liwicki and H. Bunke, “Handwriting recognition of whiteboard notes”, In Proceedings of the 12th Conference of the International Graphonomics Society, 2005, pp. 118–122
  7. V. Carbune, P. Gonnet, T. Deselaers, H. A. Rowley, A. N. Daryin, M. Calvo, L. Wang, D. Keysers, S. Feuz, and P. Gervais, “Fast multi-language lstm-based online handwriting recognition”, Int. J. DocumentAnal. Recognit., vol. 23, no. 2, pp. 89–102, 2020
  8. X.-Y. Zhang, Y. Bengio, and C.-L. Liu, “Online and offline handwritten Chinese character recognition: A comprehensive study and new bench-mark”, Pattern Recognition, vol. 61, pp. 348–360, 2017
  9. S. Jager, S. Manke, J. Reichert, and A. Waibel, “Online handwriting recognition: the NPen++ recognizer”, IJDAR, vol. 3, no. 3, pp. 169–180, 2001
  10. S. Garcia-Salicetti, B. Dorizzi, P. Gallinari, and Z. Wimmer, “Maximum Mutual information training for an online neural predictive handwritten word recognition system”, IJDAR, vol. 4, no. 1, pp. 56–68, 2001
  11. E. Caillault and C. Viard-Gaudin, “Mixed discriminant training of hybrid ANN/HMM systems for online handwritten word recognition”, IJPRAI,vol. 21, no. 1, pp. 117–134, 2007
  12. T. M. T. Do and T. Arti`eres, “Maximum margin training of gaussian HMMs for handwriting recognition”, in 10th International Conference On Document Analysis and Recognition, ICDAR 2009, Barcelona, Spain, 26-29 July 2009, 2009, pp. 976–980
  13. J. Schenk and G. Rigoll, “Novel hybrid NN/HMM modelling techniques for on-line handwriting recognition”, in 10th International Workshop on Frontiers in Handwriting Recogn