Toward Zero-shot Character Recognition: A Gold Standard Dataset with
  Radical-level Annotations
Xiaolei Diao Daqian Shi Jian Li Lida Shi Mingzhe Yue Ruihua Qi Chuntao Li Hao Xu
Optical character recognition (OCR) methods have been applied to diverse
tasks, e.g., street view text recognition and document analysis. Recently,
zero-shot OCR has piqued the interest of the research community because it
considers a practical OCR scenario with unbalanced data distribution. However,
there is a lack of benchmarks for evaluating such zero-shot methods that apply
a divide-and-conquer recognition strategy by decomposing characters into
radicals. Meanwhile, radical recognition, as another important OCR task, also
lacks radical-level annotation for model training. In this paper, we construct
an ancient Chinese character image dataset that contains both radical-level and
character-level annotations to satisfy the requirements of the above-mentioned
methods, namely, ACCID, where radical-level annotations include radical
categories, radical locations, and structural relations. To increase the
adaptability of ACCID, we propose a splicing-based synthetic character
algorithm to augment the training samples and apply an image denoising method
to improve the image quality. By introducing character decomposition and
recombination, we propose a baseline method for zero-shot OCR. The experimental
results demonstrate the validity of ACCID and the baseline model quantitatively
and qualitatively.