A character recognizer for Turkish language

Korkmaz, Şadiye; Kircicegi, G.; Akinci, Y.; Atalay, Volkan

doi:10.1109/icdar.2003.1227855

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Research about recognition of handwritten Turkish text is very limited. There are studies on offline Turkish character recognition with some constraints applied on the style or the case of writing [29], [30], [31]. In [32], a HMM system which was previously developed for English, is used for offline handwritten Turkish text recognition.…”

Section: Related Work (İli̇şki̇li̇ çAlişmalar)mentioning

confidence: 99%

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Recognition of Online Turkish Handwriting using Transfer Learning

BİLGİN TAŞDEMİR

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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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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Section: Related Work (İli̇şki̇li̇ çAlişmalar)mentioning

confidence: 99%

“…The reported recognition rate is 84% using a lexicon of size 2,500. [31] proposes a machine printed character recognizer developed using ANNs. The character recognition rate is reported as 95.2% for a proprietary dataset.…”

Section: Related Work (İli̇şki̇li̇ çAlişmalar)mentioning

confidence: 99%

Recognition of Online Turkish Handwriting using Transfer Learning

BİLGİN TAŞDEMİR

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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“…There is little research about recognition of handwritten Turkish text. A number of studies cover offline Turkish character recognition with some constraints applied on the style or the case of writing [24][25][26]. In offline handwritten Turkish text recognition, Yanikoglu and Kholmatov [27] use the HMM letter models previously developed for English, by mapping the Turkish characters to the closest English character (the input of the word güneş is recognized as gunes).…”

Section: Related Workmentioning

confidence: 99%

Large vocabulary recognition for online Turkish handwriting with sublexical units

Taşdemir¹,

Yanıkoğlu²

2018

Turk J Elec Eng & Comp Sci

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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.

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“…Research about recognition of handwritten Turkish text is very limited. There are studies on offline Turkish character recognition with some constraints applied on the style or the case of writing (Çapar et al, 2003;Kaplan et al, 2017;Korkmaz et al, 2003). In (Yanikoglu and Kholmatov, 2003), a HMM system which was previously developed for English, is used for offline handwritten Turkish text recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The reported recognition rate is 84% using a lexicon of size 2,500. (Korkmaz et al, 2003), proposes a machine printed character recognizer developed using ANNs. The character recognition rate is reported as 95.2% for a proprietary dataset.…”

Section: Introductionmentioning

confidence: 99%

Online Turkish Handwriting Recognition Using Synthetic Data

Taşdemir¹

2022

European Journal of Science and Technology

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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.

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A character recognizer for Turkish language

Cited by 5 publications

References 6 publications

Recognition of Online Turkish Handwriting using Transfer Learning

Recognition of Online Turkish Handwriting using Transfer Learning

Large vocabulary recognition for online Turkish handwriting with sublexical units

Online Turkish Handwriting Recognition Using Synthetic Data

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