@techreport{oai:ipsj.ixsq.nii.ac.jp:00231460,
 author = {吉田, 有里 and 粟野, 愛未 and 松本, 尚 and Yuri, Yoshida and Manami, Awano and Takashi, Matsumoto},
 issue = {6},
 month = {Dec},
 note = {現在広く活用されている文字認識技術の多くは文字領域の検出と文字領域ごとの文字認識を別々の処理で行い，日本語の文字認識に関しては，文字数分だけ繰り返して認識処理を行う方式が主流である．一方物体検出モデルである YOLO（You Only Look Once）は，単一の畳み込みネットワークで複数の物体検出と認識を同時に行うことができる．そこで我々は YOLO を文字認識に応用することを提案する．本論文では，白背景に黒文字のみを含む日本語の文書画像について，3000 種類を超える文字の高精度な認識が YOLO で可能なことを実証する．実証実験の結果，文字は一般的な物体検出の検出対象よりも遥かに種類が多く似通ったものばかりであるが，数千種類の文字を対象としても YOLOv3 を用いて高精度で一括認識を行うことが可能であると分かった．, Most of today's widely used character recognition technologies perform character region detection and character recognition for each character region in separate processes, and especially for Japanese character recognition, the mainstream method is to repeat the recognition process for the number of characters. On the other hand, YOLO (You Only Look Once), an object detection model, can simultaneously detect and recognize objects in a single convolutional network. We propose an application of YOLO to character recognition. In this paper, we demonstrate that YOLO can accurately recognize more types of characters on document images that contain only black text on a white background. Our experimental results show that even though characters come in many more diverse and similar forms compared to typical objects targeted in object detection, YOLO can be used for high-accuracy batch recognition of thousands of different characters.},
 title = {YOLOを用いた多数文字一括認識の有効性について},
 year = {2023}
}