@techreport{oai:ipsj.ixsq.nii.ac.jp:00231459,
 author = {粟野, 愛未 and 吉田, 有里 and 松本, 尚 and Manami, Awano and Yuri, Yoshida and Takashi, Matsumoto},
 issue = {5},
 month = {Dec},
 note = {近年，様々な手法の文字認識が普及しているが，その多くは文字領域の切り出しと個別文字の認識を分けて行うため，前処理等の多くの工程と認識作業の繰り返しを必要とする．我々は，これらの処理は深層学習による物体検出器を使用すれば不要であり，特にレイアウト解析のような背景から文章領域を切り出す処理が省略できると考えた．本論文では，YOLO（You Only Look Once）という物体検出器を使って文字検出と文字認識を一斉に行うことにより，文章領域を特定する前処理を用いずとも高い精度かつ一括で文字を認識できることを実証する．カラー背景・カラー文字の画像やイラストと文字が混在する画像を用いて YOLOv3 で学習した結果，画像内の文字の内正しく認識できた文字の割合はおよそ 99.40% となった．, In recent years, various methods for character recognition have become popular, but most of them require a lot of preprocessing and repetition of recognition tasks because they separate the character region segmentation and the recognition of individual characters. We believe that these processes are unnecessary when an object detector based on deep learning is used, and in particular, the process of extracting the text area from the background, such as layout analysis, can be omitted. In this paper, we demonstrate that character detection and recognition can be performed simultaneously using an object detector called YOLO (You Only Look Once), and that characters can be recognized in a batch with high accuracy without using preprocessing to identify text regions. The results of training with YOLOv3 on images with colored backgrounds and colored text, and on images with a mixture of illustrations and text, showed that the percentage of correctly recognized characters in the images was approximately 99.40%.},
 title = {イラストや図を含む一般文書からのYOLOを用いた文字認識},
 year = {2023}
}