Handwritten Chinese Character Recognition Using Deep Neural Networks
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
16-5-2019
End Date
16-5-2019
Abstract
Chinese script is utilized by one-sixth of the world’s population. Unlike Latin, consisting only of twenty-six characters, the Chinese script comprises approximate ninety thousand characters. Due to the tremendous variation among these calligraphically rather complex characters, it is worthwhile for researchers to design and build fast and efficient technologies to classify handwritten Chinese characters. Recently, deep neural networks gained tremendous success in object recognition and different Latin based character recognition which led us to the idea to conduct several research attempts in this area by considering convolutional neural networks to recognize this complex script. To validate the efficiency and the robustness of the proposed Chinese character recognition system the CASIA HWDB1.1 database was considered. This dataset is a benchmark used by the research community to assess different handwriting recognizers. The dataset - built by 300 different writers - is considered a large one containing 1.1 million images split into 3755 most commonly used Chinese characters. Our various experiments culminated at 95% accuracy, which is a very promising result and comparable with the current state-of-the-art results.
Recommended Citation
Song, Jia, "Handwritten Chinese Character Recognition Using Deep Neural Networks" (2019). Symposium Of University Research and Creative Expression (SOURCE). 177.
https://digitalcommons.cwu.edu/source/2019/Oralpres/177
Department/Program
Computer Science
Slides for SOURCE 2019 presentation Song
Additional Files
Jia_Handwritten Chinese Character Recognition Using Deep Neural Network.pdf (464 kB)Slides for SOURCE 2019 presentation Song
Handwritten Chinese Character Recognition Using Deep Neural Networks
Ellensburg
Chinese script is utilized by one-sixth of the world’s population. Unlike Latin, consisting only of twenty-six characters, the Chinese script comprises approximate ninety thousand characters. Due to the tremendous variation among these calligraphically rather complex characters, it is worthwhile for researchers to design and build fast and efficient technologies to classify handwritten Chinese characters. Recently, deep neural networks gained tremendous success in object recognition and different Latin based character recognition which led us to the idea to conduct several research attempts in this area by considering convolutional neural networks to recognize this complex script. To validate the efficiency and the robustness of the proposed Chinese character recognition system the CASIA HWDB1.1 database was considered. This dataset is a benchmark used by the research community to assess different handwriting recognizers. The dataset - built by 300 different writers - is considered a large one containing 1.1 million images split into 3755 most commonly used Chinese characters. Our various experiments culminated at 95% accuracy, which is a very promising result and comparable with the current state-of-the-art results.
https://digitalcommons.cwu.edu/source/2019/Oralpres/177
Faculty Mentor(s)
Szilard Vajda