TY - GEN
T1 - Recognition of Japanese historical hand-written characters based on object detection methods
AU - Tang, Yiping
AU - Hatano, Kohei
AU - Takimoto, Eiji
N1 - Funding Information:
We thank the anonymous reviewers for helpful comments. This work was supported by JSPS KAKENHI Grant Numbers JP18K18508, JP19H04174 and JP19H04067, respectively.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/9/20
Y1 - 2019/9/20
N2 - We consider the recognition problem of Japanese historical handwritten characters called “Kuzushiji”. Unlike modern characters, Kuzushiji characters are harder to recognize partly because many of them are connected and not separated by spaces without any segmentation information. We propose two methods for segmentation and recognition of Kuzushiji characters. The first method learns segmentation rules and character classifiers simultaneously from data sets with character labels and segmentation information. Second method is for segmentation and can be used with any single character recognizer. Our methods outperform other baselines and achieve the state-of-the-art accuracy on both segmentation and recognition tasks on data sets of three consecutive Kuzushiji characters.
AB - We consider the recognition problem of Japanese historical handwritten characters called “Kuzushiji”. Unlike modern characters, Kuzushiji characters are harder to recognize partly because many of them are connected and not separated by spaces without any segmentation information. We propose two methods for segmentation and recognition of Kuzushiji characters. The first method learns segmentation rules and character classifiers simultaneously from data sets with character labels and segmentation information. Second method is for segmentation and can be used with any single character recognizer. Our methods outperform other baselines and achieve the state-of-the-art accuracy on both segmentation and recognition tasks on data sets of three consecutive Kuzushiji characters.
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U2 - 10.1145/3352631.3352642
DO - 10.1145/3352631.3352642
M3 - Conference contribution
AN - SCOPUS:85074780655
T3 - PervasiveHealth: Pervasive Computing Technologies for Healthcare
SP - 72
EP - 77
BT - Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019
PB - ICST
T2 - 5th International Workshop on Historical Document Imaging and Processing, HIP 2019, held in conjunction with ICDAR 2019
Y2 - 20 September 2019 through 21 September 2019
ER -