Handwritten hiragana classifier with minimal training data utilizing convolutional neural networks

Adrián Chouza; Raúl Hernández; Jose Jimenez; Ulises Orozco-Rosas; Kenia Picos

doi:10.1117/12.2528046

6 September 2019 Handwritten hiragana classifier with minimal training data utilizing convolutional neural networks

Adrián Chouza, Raúl Hernández, Jose Jimenez, Ulises Orozco-Rosas, Kenia Picos

Proceedings Volume 11136, Optics and Photonics for Information Processing XIII; 111360L (2019) https://doi.org/10.1117/12.2528046
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

A robust algorithm for Japanese handwritten hiragana character classifier is proposed using a machine learning approach for minimal training data to reduce computational power and time consumption. The proposed algorithm utilizes image recognition techniques to process samples from a data set. Six different models involving convolutional neural networks are implemented using image templates that were previously processed, in order to achieve great results with the least possible amount of training data. Prediction results were evaluated separating the dataset in training and validation data at a ratio of 5:95 respectively, achieving 96.95% as the highest accuracy across different models, competing against state-of-the-art classifiers with 80:20 training ratio.

Conference Presentation

Citation Download Citation

Adrián Chouza, Raúl Hernández, Jose Jimenez, Ulises Orozco-Rosas, and Kenia Picos "Handwritten hiragana classifier with minimal training data utilizing convolutional neural networks", Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360L (6 September 2019); https://doi.org/10.1117/12.2528046

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