## 2 Background Study  
Various researches have been carried out for the extraction of PDF into text. One of them deals only with the extraction of metadata from PDF files. In [24], the authors propose a framework for extracting metadata from PDF files, developed using the Java language, which they claim is 9 to 10 times faster than other available tools. In this work, the authors focused on metadata extraction. However, they did not measure how much text is extracted as a complete sentence. According to the study of [25], there are two classifications for information extraction: one is metadata extraction, and another is key-insights extraction. For the extraction of metadata and key insights, there are various computational approaches such as Deep Learning, Naive Bayes classification, Support Vector Machine (SVM), Conditional Random Fields (CRF), Hidden Markov Models (HMM), and rule-based approaches [25]. However, this study also does not suggest any specific method or combination of methods or techniques to get text data from PDF documents with proper sentence boundary. [PERSON] et al. [26], proposed a novel system called pdf2 latex that uses deep learning methods to extract mathematical expressions and plain text into the latex format and achieves 81.1% accuracy. In [27], the authors conducted a comparison between four different commercially available optical character recognition software in converting Hansard Malaysia parliamentary reports. In this study, the author also focused on the correctly recognized characters on each page of the PDF document, and no sentence boundaries were considered. [PERSON] et al. [21] proposed a rule-based PDF extraction tool called \"Icicite\", which can detect paragraph boundaries, word boundaries, reading order, and body-text paragraphs. However, the authors did not mention sentence boundaries in their work and compared 14 PDF extraction tools. The 14 tools mentioned in this study, including the proposed \"Icicite\" tool, work to detect paragraph boundaries, line breaks, and characters but not to detect proper sentences. In addition, this study presents a comparative evaluation of several tools, including PdfHotext, Pdfminer, and Grobid, which also appear in our study. Of these three tools, Grobid performed better, which is also evident from our experimental results. The study by [PERSON] et al. [28] presented the evaluation of Tika, DocToText, and Xpdf tools. Among these tools, Tika achieved 58% accuracy in extracting text from PDF documents, in orderly extraction, which is close to our experimental result. The official benchmark for the Pymudf tool is included in the official documentation [29] of this tool. Our experimental results also support the benchmark in terms of the tool's text extraction capabilities. [PERSON] et al. [30] experimented with extracting metadata from scientific PDF documents using various tools and techniques. In this experiment, the authors used seven different tools for extracting metadata from scientific PDF documents and provided the comparative experimental results in terms of accuracy. In their experiment, the Groblid [31] tool performed better than the tools used by pdfttotext [32] tool to extract text from PDF documents in extracting metadata than the other six tools, with 92% accuracy in title extraction. Groblid also performed better than the other tools in extracting other metadata, including authors, authors' last names, abstract, and year. [PERSON] et al. [33] proposed a tool called OCR++ that also extracts metadata from the scientific literature. The authors made a comparison between grobid and the tool oct++. In many cases, including title, author's first name, last name, and section heading extraction grobid performed better than the proposed system in terms of f-1 score. However, this study does not provide comparative results or suggest a technique for extracting sentence boundaries from scientific PDF documents. [PERSON] [34] conducted a study of sentence boundary detection in legal texts.