## 4 Results and Discussion
This means that, on average, 81% of the text extracted by Grobid matched the original text created manually by the authors for the 10 documents used in this study. After Grobid, Pymupdf achieved a 57% match, which is significantly lower than the score achieved by Grobid. The performance of text extraction by the Grobid tool is also evident from the individual results for all documents. For each document, Grobid achieved a much better match rate than the other tools with which it was compared. In addition to sentence boundary extraction in conjunction with NLP frameworks, Grobid also performs better in pure PDF- to-text conversion. In [27], the authors compared four industrial optical character recognition programs for text content extraction with the character recognition-based measure, but they did not experiment with sentence boundary identification. This study showed good performance in character recognition, but was not promising in sentence boundary identification. [PERSON] et al. [21] compared 14 PDF tools for text extraction and measured their performance on paragraph boundaries, distinguishing body text from non-body text, word boundaries, and reading sequences. They did not perform the sentence boundary extraction task. However, Grobid performed better than the other tools in identifying paragraph boundaries. This trend is also reflected in our study, where Grobid performed better than other tools in converting PDF-to-text and extracting sentence boundaries using the spacy nlp framework. The PDF2 LaTex [26] tool proposed by [PERSON] et al. achieved 81% accuracy in converting mathematical expressions and text. However, from our experimental results, the Grobid tool performs better than PDF2 LaTex in terms of accuracy, and the results with the NLTK, Spacy, and Gensim frameworks are 88%, 91%, and 86%, respectively. In [30; 33], the authors experimented with extracting metadata, including titles and section headings. In both studies, Grobid was found to perform better in converting PDF-to-text compared to other tools. In addition, the f-1 values from these studies are almost identical to our experimental results, suggesting that Grobid performs better than other tools in other studies for different domains' data. Considering the discussions, it can be deduced that the Grobid package can be used for better text extraction from scientific PDF documents than the other five packages and that the NLP package Spacy can be used for better identification of complete sentences from the extracted text than NLTK and Gensim. Figure 2 shows the comparison results for different performance metrics obtained for different PDF text extraction packages by NLP packages. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Document** & **Grobid** & **Pdfminer.six** & **Pdftotext** & **Pymupdf** & **Pypdf2** & **Tika** \\ \hline P1 & 0.92 & 0.76 & 0.19 & 0.84 & 0.19 & 0.79 \\ P2 & 0.72 & 0.21 & 0.10 & 0.22 & 0.10 & 0.22 \\ P3 & 0.87 & 0.64 & 0.23 & 0.69 & 0.13 & 0.66 \\ P4 & 0.88 & 0.66 &  
Another goal of this study is to determine the memory requirements and runtime of all packages used to extract PDF-to-text. Tables 5 and 6 show the memory usage and runtime results for all packages used in this study for the 10 ground truth documents. From Table 6, it can be seen that among all the PDF-to-text extraction packages, Pymupdf takes the least runtime of 1.23 s to extract text from 10 PDF documents, while Grobid has the highest runtime of all the compared packages at 36.5 s. On the other hand, Grobid requires the least memory with 46.13 megabytes, and Tika requires the highest memory with 159.46 megabytes among all the compared packages as shown in Table 5. Figures 3 and 4 show the visual representation of the memory usage and runtime of all compared packages. The runtime result is also supported by the work described in [21]. From the result presented in [21], Grobid took 42 s on average to convert a single file, and Pdftotext took 0.3 s per file.