Abstract of doctoral dissertation Computer science: Enhancing performance of mathematical expression detection in scientific document images

The thesis mainly aims to solve the following tasks: Firstly, the thesis extensively analyzes a wide range of existing approaches for the ME detection in scientific document images. Then, the thesis investigates and proposes novel methods to improve the detection accuracy of MEs. After enhancing the detection accuracy of MEs, the thesis investigates and pro poses a framework to improve the accuracy of the recognition of MEs in scientific document images.
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Abstract of doctoral dissertation Computer science: Enhancing performance of mathematical expression detection in scientific document images MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF SCIENCE AND TECHNOLOGY BUI HAI PHONGENHANCING PERFORMANCE OF MATHEMATICAL EXPRESSION DETECTION IN SCIENTIFIC DOCUMENT IMAGES Major: Computer Science Code: 9480101 ABSTRACT OF DOCTORAL DISSERTATION COMPUTER SCIENCE Hanoi −2021 This study is completed at: Hanoi University of Science and Technology Supervisors: 1. Assoc. Prof. Hoang Manh Thang 2. Assoc. Prof. Le Thi Lan Reviewer 1: Reviewer 2: Reviewer 3: This dissertation will be defended before approval commitee at Hanoi University of Science and Technology: Time , date month year 2021This dissertation can be found at: 1. Ta Quang Buu Library - Hanoi University of Science and Technology 2. Vietnam National Library INTRODUCTIONMotivation Up to now, a huge number of scientific documents have been produced. Scientific doc-uments have provided valuable information for research community. The documents need tobe digitized to allow users to retrieve information efficiently. Recently, most documents havebeen published in the PDF format. However, a large number of documents have been stillavailable in raster format. It is obvious that the PDF processing techniques cannot be appliedfor such raster document images. We need to apply image processing for the digitization of thedocument images. The key steps of the document digitization are: document analysis, opticalcharacter recognition and content searching [2]. The digitization of standard text rich docu-ments has considered as a solved problem. However, the digitization of scientific documentsthat contained rich MEs is a non trivial task. Actually, scientific documents usually consist ofheterogeneous components: tables, figures, texts and MEs. In scientific documents, MEs maybe mixed with various components and sizes, styles of MEs may frequently vary. Therefore,the improvement of accuracy of the detection and recognition of MEs is an important step ofthe digitization of scientific documents. Inspired by the above ideas, the thesis mainly aimsto improve the accuracy of detection and recognition of MEs in scientific document images.Introduction of ME detection and recognition in document images In mathematics, an expression or mathematical expression is a finite combination ofsymbols that is well-formed according to rules that depend on the context [5]. In scientificdocuments, MEs are classified in two categories, i.e. isolated (displayed) and inline (embedded)expressions. Isolated expressions display in separate lines, meanwhile inline expressions aremixed with other components in document pages, e.g. texts and figures. The detection of expressions aims to locate MEs in document images. Meanwhile, therecognition of MEs aims at converting expressions from image format to string (representationin Latex). An example of ME detection and recognition is illustrated in Figure 1. Actually,the detection and recognition of MEs in document images are closely related. The accuracy ofthe detection allows to obtain accuracy of the recognition. In contrast, the incorrect detectionmay cause errors in the recognition of MEs. The hypotheses of the thesis are assumed as follows: (1) The thesis focuses on the de-tection and recognition of MEs in scientific document images that have been written in aformal way. The thesis aims to detect MEs in the body of documents, the detection of MEscontained in other document components such as tables, figures are actually investigated inother problems (table or figure detection). Moreover, the size of MEs should not pass the sizeof the whole documents. (2) Scientific documents can be generated in various ways: camera 1Figure 1 Example of the detection (a) and a detected ME in a document image (b). Isolatedand inline MEs are denoted in red and blue, respectively. Extracted ME is recognized andrepresented using Latex (c).captured images, handwritten documents, scanned format or PDF conversion. Moreover, thedetection accuracy highly depends on the quality of the documents. Like conventional meth-ods in document analysis, the thesis focuses on the detection of MEs in document imagesthat are scanned at high resolution and non-skew. (3) The detection of MEs is represented bybounding boxes. Then the detected MEs are recognized and represented in Latex format [4]. Main challenges of the recognition of MEs can be described as follows: (1) Accuraterecogni ...