## 5 Conclusions  
This article contains a detailed comparison of different Python packages for PDF-to-text extraction with their runtime and memory requirements for documents from the EDLC domain. It also presents the precision measure of correctly identified sentences from the extracted texts using three widely used natural language frameworks, namely NLTK, Spacy, and Gensim. The Grobid PDF-to-text extraction package achieved the highest f-1 score of 93% using NLP framework Spacy, while NLTK and Gensim achieved 88% and 86% precision values, respectively. Grobid also requires the least amount of memory to convert PDF documents to text. This study makes it easy for researchers working with text processing from the EDLC domain to choose the best combination of tools and techniques  
Figure 5: Comparison results of run times in seconds of different PDF-to-text extraction packages for 10, 50, and 100 documents (a lower score is better).  
Figure 6: Comparison results of memory usage in Mega Bytes of different PDF-to-text extraction packages for 10, 50, and 100 documents (a lower score is better).  
for extracting text with correct sentence boundaries from PDF documents. With the help of this work, EDLC researchers would waste less time in selecting appropriate tools and techniques for extracting sentence-level text from PDF documents. This study is also of great help in developing automated EDLC related information systems that need to extract text from PDF documents and use sequence labels, for example, an automated chemical information system that features automatic entity extraction elated to EDLC from the scientific literature. For this type of system, the text data needs to be as accurate as possible, and in this scenario, the current work can be very useful in extracting and creating clean text inputs from PDF documents. This study has been carried out using documents related to Electric Double Layer Capacitors. However, this study can also be carried out using scientific documents from other fields. The results can be analyzed for new insights, including domain-specific precision, recall, and f1 values, as well as differences in runtime, memory usage, and usage of other computational resources, including CPU utilization, thermal impact, and power consumption.  
Conceptualization, M.S.U.M. and J.S.; methodology, M.S.U.M. and J.S.; software, M.S.U.M. and T.B.S.; validation, K.Z.Z. and R.J.; formal analysis, M.S.U.M.; investigation, T.B.S.; resources, A.N. and F.A.; data curation, M.S.U.M.; writing--original draft preparation, M.S.U.M.; writing--review and editing, J.S. and R.J.; visualization, M.S.U.M.; supervision, J.S.; project administration, K.Z.Z.; funding acquisition, A.N. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Trans-Disciplinary Research Grant Scheme of the Ministry of Higher Education in Malaysia entitled: An Artificial Neural Network Sine Cosine Algorithm-based Hybrid Prediction Model for the Production of Cellulose Nanocrystals from Oil Palm Empty Fruit Bunch TRGS/1/2018/ UMP /01/1/4 (RDU191801-4), RDU182201 and The APC was fully funded by the Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54782, Pakistan.  
Not applicable.  
The data presented in this study are available on request from the corresponding author or by opening an issue at [[https://github.com/ping543f/pdf-extraction-tool-comparison](https://github.com/ping543f/pdf-extraction-tool-comparison)]([https://github.com/ping543f/pdf-extraction-tool-comparison](https://github.com/ping543f/pdf-extraction-tool-comparison)) repository. The data are not publicly available due to the total implementation of the information system is still in progress.  
The authors declare no conflict of interest.