  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.
