Unsupervised Extraction of Training Data for Pre-Modern Chinese OCR

Many mainstream OCR techniques involve training a character recognition model using labeled exemplary images of each individual character to be recognized. For modern printed writing, such data can be easily created by automated methods such as rasterizing appropriate font data to produce clean example images. For historical OCR in printing and writing styles distinct from those embodied in modern fonts, appropriate character images must instead be extracted from actual historical documents to achieve good recognition accuracy. For languages with small character sets it may feasible to perform this process manually, but for languages with many thousands of characters, such as Chinese, manually collecting this data is often not practical.