## 2 Background Study
The author compared three techniques, including Punkt Model with user-defined abbreviations, Conditional Random Fields (CRF), and Deep Learning Neural Network (DNN). For the Punkt model, the author adapted the NLTK punkt method and achieved an accuracy of 72% in recognizing sentence boundaries, which is significantly lower than the result of CRF and DNN, which achieve 93% and 89%, respectively. The sentence boundary detection experiment was conducted with plain-text corpora converted from json format and does not include PDF extraction for corpora generation. Among the existing NLP tools, only the NLTK tool is used in this study. The other tools are based on the machine learning approach used in this study. [PERSON] [35] compared two machine learning-based methods, namely CRF and BiLSTM-CRF, for sentence boundary detection in Thai sentences. In this study, a plain text corpus from Thai web forums consisting of online product review data was used. This study used plain text data rather than data extracted from a PDF file. In addition, this study did not use existing sentence boundary detection tools, but the author developed his own tool based on machine learning. In this study, the CRF-based approach provided better results than the BiLSTM-CRF approach. Most studies found on PDF-to-text extraction focus on extracting metadata and text content from scientific literature in PDF format. The extraction is done while preserving the structure of the paper, e.g., extracting text content from the introduction section of a paper and labeling or identifying the extracted text as an introduction or just the metadata including author names, title, year, and abstract. However, very few studies mentioned techniques or tools for extracting sentence boundaries. The studies that do mention sentence boundary extraction use custom tools based on machine learning approaches and use plain text datasets from fields other than materials science, especially EDLC, which do not involve text extraction from PDF documents. In this scenario, two questions need to be answered:  
* Is the extracted textual content in a complete sentence form? * Is a single sentence treated as multiple sentences? Therefore, answering these two questions provided the opportunity for this research to discover the appropriate tools and techniques for extracting sentence boundaries from scientific PDF documents, using a combination of existing PDF-to-text conversion tools and NLP frameworks, in an attempt to reduce the time required by researchers to find the appropriate combinations. Complete sentence recognition plays an important role in an automatic text processing system because a complete sentence contains both syntactic and semantic information. Different studies have used different tools and techniques for PDF extraction and sentence boundary detection in the creation of different chemical or material information systems, without providing a rationale for their choice of tools and the performance of the tools for PDF extraction and sentence boundary detection [36; 37; 38]. Therefore, a comparison between the existing PDF and text extraction tools is required to find a suitable one. Therefore, this study will be useful to EDLC researchers in their decisions about PDF extraction tools and NLP packages. As far as we know, there is no formal analysis to date that compares various existing PDF-to-text conversion tools in conjunction with sentence boundary detection tools. More precisely, there is no work that deals with sentence boundary extraction from scientific literature in the EDLC domain. Thus, the novelty of this study lies in the comparative analysis of different tools and techniques for PDF-to-text conversion and sentence boundary extraction for the EDLC domain.