During past decades, the field of Document Image Analysis and Recognition (DIAR) has been the subject of many researches due to its wide range of applications. DIAR can be applied to either printed or handwritten, textual or graphical document images with the purpose of automatically analyzing their contents in order to retrieve useful information [1, 2]. The applications of DIAR arise in different fields such as the storage and indexing of cultural heritage by analyzing historical manuscripts. Text detection and recognition in imagery are two key components of most techniques in DIAR [3, 4]. Since the existing methods for text detection rely on texture estimation [5] or edge detection [6] as stated by Wolf et al. [7], the text characteristics may affect the document analysis. For this reason, text recognition pipelines typically resize text lines to a specific height which is the one they were trained
on.
In this thesis, the influence of the text height on document analysis is investigated. Document resizing
to a specific text height will be inserted as first step of several DIAR methods for running experiments. The thesis consists of the following milestones:
• Producing a data set with text height labeled for a sufficient amount of ancient books and
manuscripts [8, 9].
• Developing a system which detects text in the documents and resizes it to a predetermined height
in pixels.
• Running various experiments to determine whether this improves the results of different DIAR
methods.
[1] Deepika Ghai and Neelu Jain. Text extraction from document images-a review. International Journal of Computer Applications, 84(3), 2013.
[2] Vikas Yadav and Nicolas Ragot. Text extraction in document images: Highlight on using corner points. In 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pages 281–286, 2016.
[3] Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, and Jiajun Liang. East: an efficient and accurate scene text detector. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 5551–5560, 2017.
[4] Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J Wu, and Andrew Y Ng. Text detection and character recognition in scene images with unsupervised feature learning. In 2011 International Conference on Document Analysis and Recognition, pages 440–445. IEEE, 2011.
[5] Bangalore S Manjunath and Wei-Ying Ma. Texture features for browsing and retrieval of image data. IEEE Transactions on pattern analysis and machine intelligence, 18(8):837–842, 1996.
[6] Chung-Ching Chen et al. Fast boundary detection: A generalization and a new algorithm. IEEE Transactions on computers, 100(10):988–998, 1977.
[7] Christian Wolf, Jean-Michel Jolion, and LIRIS INSA de Lyon. Model based text detection in images and videos: a learning approach. Laboratoire dInfoRmatique en Images et Systemes dinformation, Palmas, TO, 2004.
[8] Vincent Christlein, Anguelos Nicolaou, Mathias Seuret, Dominique Stutzmann, and Andreas Maier. Icdar 2019 competition on image retrieval for historical handwritten documents. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1505–1509. IEEE, 2019.
[9] https://lme.tf.fau.de/competitions/icdar-2021-competition-on-historical-document-classification.