Unfortunately, these approaches cannot just be adopted to historical Chinese newspapers. As we have shown, especially complex layout and resulting difficulties of reliable automatic page segmentation have so far prevented full text generation of these newspapers even within China (Arnold, 2021; Arnold, forthcoming; Arnold et al., forthcoming). In this long abstract we present the first results from a systematic approach towards full text extraction from a Republican China newspaper ( 1). Our basis is a small corpus for which text ground truth exists. We present our character segmentation method which produces about 90.000 images of characters. Based on the hypothesis that pre-training on extensive amounts of suitably augmented character images will increase the OCR accuracy for evaluation on real-life character image data, we generate synthetic training data. We then compare the OCR recognition results and show that a combination of synthetic and real characters produces best results. Finally, we propose a method that makes use of a masked language model for OCR error correction.