Can I extract text from SEC 10-K and 10-Q filings downloaded from EDGAR?

Yes — EDGAR PDF filings are digitally created and contain an embedded text layer. They are fully compatible with this tool. Very old filings may be scanned images and are not supported.

Will financial tables like income statements extract correctly?

Table text is extracted, but visual table structure (rows and columns) is not preserved — output is linear plain text. For structured table extraction from financial PDFs, a dedicated table extraction tool is more appropriate.

How many earnings reports can I process at once for a peer group analysis?

Up to 50 PDFs per batch. A typical S&P 500 sector peer group analysis covering 20–30 companies fits in a single batch including both 10-K and earnings transcript PDFs.

Is the output compatible with Python NLP libraries like spaCy or NLTK?

Yes — UTF-8 plain text is the standard input format for both libraries. Load each .txt file as a document object and apply your pipeline directly.

Convert Earnings Reports and SEC Filings to Plain Text for Quantitative Analysis

Financial analysts running text-based analysis on earnings calls, 10-Ks, or sell-side research reports hit the same wall every time: the data is locked in PDFs. Deliteful extracts the embedded text from financial PDFs into clean UTF-8 files — ready for sentiment scoring, keyword frequency analysis, or feeding into a financial NLP model.

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Quantitative and fundamental analysts increasingly use text analysis alongside traditional financial metrics — tracking management tone across earnings transcripts, monitoring regulatory language in 10-Q filings, or building keyword signals from analyst reports. SEC EDGAR delivers most filings as PDFs, and earnings call transcripts from services like Seeking Alpha or Bloomberg arrive the same way. Extracting the text layer is the prerequisite step before any of that analysis can happen.

Deliteful supports PDFs up to 300 MB — large enough for full 10-K annual reports including exhibits — and processes batches of up to 50 files, which covers a typical peer group comparison or a full quarter of earnings documents. Output is UTF-8 plain text with page separators, compatible with Python (pandas, NLTK, spaCy), R, and Excel-based text analysis workflows.

How it works

1
Create a free account
Sign in with Google in about 3 clicks — no credit card required.
2
Upload financial PDFs
Add 10-Ks, earnings transcripts, analyst reports — up to 50 files per batch.
3
Extract text
Deliteful extracts all embedded text from each PDF and outputs UTF-8 .txt files.
4
Run your analysis
Feed output into Python, R, or your NLP tool of choice — page separators enable clean document chunking.

Frequently asked questions

Can I extract text from SEC 10-K and 10-Q filings downloaded from EDGAR?: Yes — EDGAR PDF filings are digitally created and contain an embedded text layer. They are fully compatible with this tool. Very old filings may be scanned images and are not supported.
Will financial tables like income statements extract correctly?: Table text is extracted, but visual table structure (rows and columns) is not preserved — output is linear plain text. For structured table extraction from financial PDFs, a dedicated table extraction tool is more appropriate.
How many earnings reports can I process at once for a peer group analysis?: Up to 50 PDFs per batch. A typical S&P 500 sector peer group analysis covering 20–30 companies fits in a single batch including both 10-K and earnings transcript PDFs.
Is the output compatible with Python NLP libraries like spaCy or NLTK?: Yes — UTF-8 plain text is the standard input format for both libraries. Load each .txt file as a document object and apply your pipeline directly.

Create your free Deliteful account with Google and extract plain text from your financial PDF corpus for NLP and quantitative analysis.