A robust model for on-line handwritten Japanese text recognition

Zhu, Bilan; Zhou, Xiangdong; Liu, Cheng‐Lin; Nakagawa, Masaki

doi:10.1117/12.807060

Cited by 5 publications

(1 citation statement)

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“…al. [24] describe a probabilistic model for recognizing text from the HANDS-kondate dataset of handwritten Japanese text [25]. Ly et.…”

Section: Handwritten Japanesementioning

confidence: 99%

Detection of Furigana Text in Images

Bjerregaard¹,

Cheplygina²,

Heinrich³

2022

Preprint

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Furigana are pronunciation notes used in Japanese writing. Being able to detect these can help improve optical character recognition (OCR) performance or make more accurate digital copies of Japanese written media by correctly displaying furigana. This project focuses on detecting furigana in Japanese books and comics. While there has been research into the detection of Japanese text in general, there are currently no proposed methods for detecting furigana.We construct a new dataset containing Japanese written media and annotations of furigana. We propose an evaluation metric for such data which is similar to the evaluation protocols used in object detection except that it allows groups of objects to be labeled by one annotation. We propose a method for detection of furigana that is based on mathematical morphology and connected component analysis. We evaluate the detections of the dataset and compare different methods for text extraction. We also evaluate different types of images such as books and comics individually and discuss the challenges of each type of image.The proposed method reaches an F1-score of 76% on the dataset. The method performs well on regular books, but less so on comics, and books of irregular format. Finally, we show that the proposed method can improve the performance of OCR by 5% on the manga109 dataset.

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“…al. [24] describe a probabilistic model for recognizing text from the HANDS-kondate dataset of handwritten Japanese text [25]. Ly et.…”

Section: Handwritten Japanesementioning

confidence: 99%