Can I use this to build a text corpus for NLP or machine learning research?

Yes. The combined output mode produces a single text file with per-document separators, making it straightforward to parse into a structured corpus. Individual .txt files work directly as input for most NLP libraries.

Will references, footnotes, and figure captions be included in the extracted text?

All embedded text in the PDF is extracted, including references, footnotes, and captions. Section order follows the PDF structure, so footnotes may appear inline or at page end depending on how the original was formatted.

What should I do if some papers in my batch are scanned images?

Scanned PDFs without embedded text will produce empty output. Run those files through an OCR tool first to generate a text layer, then re-extract. Most papers downloaded directly from publisher websites are native digital PDFs and extract without issue.

Is there a limit on how many papers I can process?

Up to 50 PDFs per batch, each up to 300 MB. A typical journal article PDF is 1–5 MB, so a 50-paper batch is well within limits and completes in under a minute.

Extract Full Text from Academic PDFs for Research Analysis

Building a text corpus from journal articles, conference papers, or dissertation PDFs is a prerequisite for systematic reviews, citation analysis, and NLP research — but manually extracting text from hundreds of PDFs is a bottleneck that stops most workflows before they start. Deliteful processes up to 50 academic PDFs simultaneously, producing plain-text files ready for quantitative analysis, topic modeling, or qualitative coding.

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Researchers conducting systematic literature reviews or computational text analysis need machine-readable text, not locked PDF content. Document preprocessing — including text extraction — is consistently one of the most time-consuming steps in large-scale review workflows. Batch extraction compresses this step dramatically: upload a folder of downloaded papers, receive one .txt per article, and import directly into NVivo, R, Python, or any text analysis environment.

Deliteful extracts embedded text faithfully, preserving section order across all pages of each paper. For multi-paper batches, the combined-file output option separates each document with clear delimiters — useful when feeding a full corpus into a language model or keyword extraction pipeline. Note that PDFs consisting entirely of scanned page images (common in older journal archives) require OCR preprocessing before text extraction will succeed.

How it works

1
Upload your paper set
Add up to 50 PDF journal articles, theses, or reports — any academic PDFs with embedded selectable text.
2
Select output format
Choose per-file for article-by-article analysis, or combined output to build a single corpus file for NLP pipelines.
3
Download and analyze
Receive clean .txt files ready to import into NVivo, Atlas.ti, R quanteda, Python NLTK, or any text analysis tool.

Frequently asked questions

Can I use this to build a text corpus for NLP or machine learning research?: Yes. The combined output mode produces a single text file with per-document separators, making it straightforward to parse into a structured corpus. Individual .txt files work directly as input for most NLP libraries.
Will references, footnotes, and figure captions be included in the extracted text?: All embedded text in the PDF is extracted, including references, footnotes, and captions. Section order follows the PDF structure, so footnotes may appear inline or at page end depending on how the original was formatted.
What should I do if some papers in my batch are scanned images?: Scanned PDFs without embedded text will produce empty output. Run those files through an OCR tool first to generate a text layer, then re-extract. Most papers downloaded directly from publisher websites are native digital PDFs and extract without issue.
Is there a limit on how many papers I can process?: Up to 50 PDFs per batch, each up to 300 MB. A typical journal article PDF is 1–5 MB, so a 50-paper batch is well within limits and completes in under a minute.