Texts, printed or written, are the most important form of cultural preservation. This is true for Chinese studies and many cultures. For this reason, optical character recognition (OCR) is one of the most important tools for heritage preservation. With OCR and reading order detection (ROD), we convert the information in their hard-copy forms to digitized documents, allowing both lasting storage and further analysis. Given a page image, the step of ROD helps us determine the order of the texts on the page. An effective ROD reduces the cost of post-OCR human labor for correction. Rule-based and n-gram models are conceivable and popular techniques for implementing ROD, considering the coordinates and the alternative orders of the individually detected characters. In this paper, we report a block-based approach that does not rely on the n-gram techniques for ROD. Instead, we identified the text blocks, and ordered the blocks based on the ideas of learning-to-rank, and concatenated the texts within the blocks by their orders. We evaluated our approach on the MTHv2 dataset. Experimental results show that our approach achieved impressive results, reducing the position error rate (PER) to 5.4%, compared to 61.7% for heuristic methods and 56.5% for MLP, representing a significant improvement of 91.3% and 90.4%, respectively. Additional experiments show that we could have used less data than one might expect to achieve a satisfactory quality of ROD. We implemented and demonstrated a fully functioning prototype at a recent AAAI conference. $$^{*}$$ The actual performance of the prototype provides solid evidence of the effectiveness and extensibility of our design to more general contexts, e.g., Japanese texts.

Chinese historical documents, with their unique layouts and reading patterns, pose significant challenges for traditional Optical Character Recognition (OCR) systems. This paper introduces a tailored OCR system designed to address these complexities, particularly emphasizing the crucial aspect of Reading Order Detection(ROD). Our system operates through a threefold process: text detection using the Differential Binarization++ model, text recognition with the SVTR Net, and a novel ROD approach harnessing raw image features. This innovative method for ROD, inspired by human perception, utilizes visual cues present in raw images to deduce the inherent sequence of ancient texts. Preliminary results show promising reductions in page error rates. By preserving both content and context, our system contributes meaningfully to the accurate and contextual digitization of Chinese historical manuscripts.