A Machine Learning Framework for Recognizing Handwritten Digits Using Convexity-Based Feature Vector Encoding-Reference-Cited by-同舟云学术

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A Machine Learning Framework for Recognizing Handwritten Digits Using Convexity-Based Feature Vector Encoding

Published:2018-10-05 Issue: Volume: Page:369-380
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Saha Sourav,Saha Sudipta,Chatterjee Suhrid Krishna,Mahapatra Priya Ranjan Sinha

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-1544-2_30

Reference19 articles.

1. Trier, O.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)

2. Yasuda, M., Fujisawa, H.: An improvement of correlation method for character recognition. Trans. IEICE Jpn. J62-D(3), 217–224 (1979)

3. Yamashita, Y., Higuchi, K., Yamada, Y., Haga, Y.: Classification of hand printed Kanji characters by the structured segment matching method. Pattern Recognit. Lett. 1, 475–479 (1983)

4. Kimura, F., et al.: Evaluation and synthesis of feature vectors for handwritten numeral recognition. IEICE Trans. Inf. Syst. E79-D(5), 436–442 (1996)

5. Tsuruoka, S., et al.: Handwritten Kanji and Hiragana character recognition using weighted direction index histogram method. Trans. IEICE Jpn. J70-D(7), 1390–1397 (1987)

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