Semantic segmentation and document layout recognition: Approaches towards full text recognition of early Chinese newspapers
Creators: name: Arnold, Matthias
Publisher: name: Zenodo
Year: 2021
Subjects: semantic segmentation
Chinese republican newspapers
document layout recognition
digital humanities

description: Abstract (cf. The Book of Abstracts, p. 11-13): The use of convolutional neural networks in digitizing historical documents has drastically expanded the quality and scope of available sources for digital analysis.[1] The ability to reuse or to refine pre-trained models means that DH practitioners are now tackling sources previously deemed impossible to process automatically. In light of these developments, we wish to present our own work on adapting tools developed for use with Western materials to the complex layouts of republican Chinese newspapers from the Early Chinese Periodicals Online (ECPO) project.[2] Our results demonstrate how cultural biases towards Latinized scripts and layouts affects models and algorithms. To overcome these shortcomings, we conducted experiments with crowd-sourcing, pattern recognition, and machine-learning. We aim to provide a development workflow from image scan to machine-readable full-text, and started to publish our ground truths for re-use.[3] Because of the dense document layout, sub-optimal image scans, special characters and varying reading directions, current OCR engines still fail to process full pages. A literature survey on Chinese approaches to the material showed that the large libraries mostly create author-title indexes for their systems.[4] We therefore decided to first create ground truth ourselves. We implemented an annotation tool to generate bounding boxes and created semantic groups (e.g. all parts an article consists of). We also applied four high-level labels (advertisement, article, image, marginalia) to boxes and groups, and trained a neural network to detect the respective areas on the pages across a larger data set. In addition, we created a double-blind keying workflow to create highquality full-text. In a next step we will use this second ground truth data to train the OCR engine and improve recognition for Republican China newspaper texts. [1] Bernhard Liebl and Manuel Burghardt, “An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers,” April 15, 2020, https://arxiv.org; Sofia Ares Oliveira, Benoit Seguin, and Frederic Kaplan, “DhSegment: A Generic Deep-Learning Approach for Document Segmentation,” in 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) (IEEE, 2018), 7–12.<br> [2] Early Chinese Periodicals Online (ECPO), https://uni-heidelberg.de/ecpo.<br> [3] Matthias Arnold and Lena Hessel, “Transforming Data Silos into Knowledge: Early Chinese Periodicals Online (ECPO),” in Heuveline, Vincent , Gebhart, Fabian Und Mohammadianbisheh, Nina (Hrsg.): E-Science-Tage 2019: Data to Knowledge (Heidelberg: heiBOOKS, 2020), 95–109, https://doi.org/10.11588/heibooks.598.c8420; Matthias Arnold, “Multilingual Research Projects: Challenges for Making Use of Standards, Authority Files, and Character Recognition,” Digital Studies / Le Champ Numérique 11 (forthcoming).<br> [4] A publication about our systematic literature survey is currently in preparation. For an introduction see Fang Zijin 方自金, ‘Status Quo, Problems and Suggestions of the Photocopying and Publishing of Newspapers in the Republic of China’ 民国报纸影印出版的现状、问题与建议, Chuban cankao 出版参考 2019, no. 6 (2019), www.sohu.com. Also, cf. Yang Jiaying 杨佳颖 and Xu Xin 许鑫, ‘Semantic Labeling of Advertising Images in Newspapers of the Republic of China Period: Illustrated by Shaoxing Opera Advertisement Published in Xinwen Bao’ 民国报纸广告图像资源的语义标注——以《新闻报》所刊的越剧广告为例, Library Journal 图书馆杂志 online first (30 June 2020): 1–11. and Xiao Hong 肖红 and Huai Yan 槐燕, ‘An Analysis of Quality Checking Problems in the Practice of Digitization of Newspapers of Republic of China’ 民国报纸数字化实践中的质检问 题探析, Tushuguan xue yanjiu 图书馆学研究, no. 07 (2017): 61-78+87.