Large vocabulary  recognition for online Turkish handwriting with sublexical units

Esma Fatima Bilgin TAŞDEMİR; Ayşe Berrin Yanikoğlu YEŞİLYURT

Makale detayları

Large vocabulary recognition for online Turkish handwriting with sublexical units

Yazarlar:

Esma Fatima Bilgin TAŞDEMİR | Ayşe Berrin Yanikoğlu YEŞİLYURT

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 5, pp.2218-2233

Belge Tipi:

Makale

Yayın Tarihi:

9/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000734673

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

We present a system for large vocabulary recognition of online Turkish handwriting, using hidden Markov models.   While using a traditional approach for the recognizer, we have identified and developed solutions for  the main problems specific to Turkish handwriting recognition.   %  First, since large amounts of Turkish handwriting samples are not available, the system is trained and optimized  using the large UNIPEN dataset of English handwriting, before extending it to Turkish using a small Turkish dataset.   %  The delayed strokes, which pose a significant source of variation in writing order due to the large number of diacritical marks in Turkish, are removed   during preprocessing.   %  Finally, as a solution to the high out-of-vocabulary rates encountered when using a fixed size lexicon in general purpose recognition,   a lexicon is constructed from sublexical units (stems and endings) learned from a large Turkish corpus. A statistical  bigram  language model learned from the same corpus is  also applied during the decoding process.      The system obtains a 91.7 % word recognition rate when tested on a small Turkish handwritten word  dataset using  a medium sized (1950 words) lexicon corresponding to the vocabulary of the test set  and 63.8 %  using a large, general purpose lexicon (130,000 words).   However, with the proposed stem+ending lexicon (12,500 words) and  bigram language model with lattice expansion,  a 67.9 % word recognition accuracy is obtained,   surpassing the results obtained with the general purpose lexicon while using a much smaller one.% that still has the same text corpus coverage.

Makale detayları

Large vocabulary recognition for online Turkish handwriting with sublexical units

Yazarlar: Esma Fatima Bilgin TAŞDEMİR | Ayşe Berrin Yanikoğlu YEŞİLYURT

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 5, pp.2218-2233

Belge Tipi: Makale

Yayın Tarihi: 9/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000734673

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Esma Fatima Bilgin TAŞDEMİR | Ayşe Berrin Yanikoğlu YEŞİLYURT

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 5, pp.2218-2233

Belge Tipi:

Makale

Yayın Tarihi:

9/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000734673

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632