# Extract Text from a PDF

You can extract text from a PDF like this:

from PyPDF2 import PdfReader

print(page.extract_text())


you can also choose to limit the text orientation you want to extract, e.g:

# extract only text oriented up
print(page.extract_text(0))

# extract text oriented up and turned left
print(page.extract_text((0, 90)))


Refer to extract_text for more details.

## Using a visitor

You can use visitor-functions to control which part of a page you want to process and extract. The visitor-functions you provide will get called for each operator or for each text fragment.

The function provided in argument visitor_text of function extract_text has five arguments: current transformation matrix, text matrix, font-dictionary and font-size. In most cases the x and y coordinates of the current position are in index 4 and 5 of the current transformation matrix.

The font-dictionary may be None in case of unknown fonts. If not None it may e.g. contain key “/BaseFont” with value “/Arial,Bold”.

Caveat: In complicated documents the calculated positions might be wrong.

The function provided in argument visitor_operand_before has four arguments: operand, operand-arguments, current transformation matrix and text matrix.

### Example 2: Extract rectangles and texts into a SVG-file

The following example converts page 3 of this PDF document into a SVG file.

Such a SVG export may help to understand whats going on in a page.

from PyPDF2 import PdfReader
import svgwrite

dwg = svgwrite.Drawing("GeoBase_test.svg", profile="tiny")

def visitor_svg_rect(op, args, cm, tm):
if op == b"re":
(x, y, w, h) = (args[i].as_numeric() for i in range(4))
dwg.add(dwg.rect((x, y), (w, h), stroke="red", fill_opacity=0.05))

def visitor_svg_text(text, cm, tm, fontDict, fontSize):
(x, y) = (tm[4], tm[5])
dwg.add(dwg.text(text, insert=(x, y), fill="blue"))

page.extract_text(
visitor_operand_before=visitor_svg_rect, visitor_text=visitor_svg_text
)
dwg.save()


The SVG generated here is bottom-up because the coordinate systems of PDF and SVG differ.

Unfortunately in complicated PDF documents the coordinates given to the visitor-functions may be wrong.

## Why Text Extraction is hard

Extracting text from a PDF can be pretty tricky. In several cases there is no clear answer what the expected result should look like:

1. Paragraphs: Should the text of a paragraph have line breaks at the same places where the original PDF had them or should it rather be one block of text?

2. Page numbers: Should they be included in the extract?

3. Headers and Footers: Similar to page numbers - should they be extracted?

4. Outlines: Should outlines be extracted at all?

5. Formatting: If text is bold or italic, should it be included in the output?

6. Tables: Should the text extraction skip tables? Should it extract just the text? Should the borders be shown in some Markdown-like way or should the structure be present e.g. as an HTML table? How would you deal with merged cells?

7. Captions: Should image and table captions be included?

8. Ligatures: The Unicode symbol U+FB00 is a single symbol ﬀ for two lowercase letters ‘f’. Should that be parsed as the Unicode symbol ‘ﬀ’ or as two ASCII symbols ‘ff’?

9. SVG images: Should the text parts be extracted?

10. Mathematical Formulas: Should they be extracted? Formulas have indices, and nested fractions.

11. Whitespace characters: How many new lines should be extracted for 3cm of vertical whitespace? How many spaces should be extracted if there is 3cm of horizontal whitespace? When would you extract tabs and when spaces?

12. Footnotes: When the text of multiple pages is extracted, where should footnotes be shown?

13. Hyperlinks and Metadata: Should it be extracted at all? Where should it be placed in which format?

14. Linearization: Assume you have a floating figure in between a paragraph. Do you first finish the paragraph or do you put the figure text in between?

Then there are issues where most people would agree on the correct output, but the way PDF stores information just makes it hard to achieve that:

1. Tables: Typically, tables are just absolutely positioned text. In the worst case, ever single letter could be absolutely positioned. That makes it hard to tell where columns / rows are.

2. Images: Sometimes PDFs do not contain the text as it’s displayed, but instead an image. You notice that when you cannot copy the text. Then there are PDF files that contain an image and a text layer in the background. That typically happens when a document was scanned. Although the scanning software (OCR) is pretty good today, it still fails once in a while. PyPDF2 is no OCR software; it will not be able to detect those failures. PyPDF2 will also never be able to extract text from images.

And finally there are issues that PyPDF2 will deal with. If you find such a text extraction bug, please share the PDF with us so we can work on it!

## OCR vs Text Extraction

Optical Character Recognition (OCR) is the process of extracting text from images. Software which does this is called OCR software. The tesseract OCR engine is the most commonly known Open Source OCR software.

PyPDF2 is not OCR software.

### Digitally-born vs Scanned PDF files

PDF documents can contain images and text. PDF files don’t store text in a semantically meaningful way, but in a way that makes it easy to show the text on screen or print it. For this reason text extraction from PDFs is hard.

If you scan a document, the resulting PDF typically shows the image of the scan. Scanners then also run OCR software and put the recognized text in the background of the image. This result of the scanners OCR software can be extracted by PyPDF2. However, in such cases it’s recommended to directly use OCR software as errors can accumulate: The OCR software is not perfect in recognizing the text. Then it stores the text in a format that is not meant for text extraction and PyPDF2 might make mistakes parsing that.

Hence I would distinguish three types of PDF documents:

• Digitally-born PDF files: The file was created digitally on the computer. It can contain images, texts, links, outline items (a.k.a., bookmarks), JavaScript, … If you Zoom in a lot, the text still looks sharp.

• Scanned PDF files: Any number of pages was scanned. The images were then stored in a PDF file. Hence the file is just a container for those images. You cannot copy the text, you don’t have links, outline items, JavaScript.

• OCRed PDF files: The scanner ran OCR software and put the recognized text in the background of the image. Hence you can copy the text, but it still looks like a scan. If you zoom in enough, you can recognize pixels.

### Can we just always use OCR?

You might now wonder if it makes sense to just always use OCR software. If the PDF file is digitally-born, you can just render it to an image.

I would recommend not to do that.

Text extraction software like PyPDF2 can use more information from the PDF than just the image. It can know about fonts, encodings, typical character distances and similar topics.

That means PyPDF2 has a clear advantage when it comes to characters which are easy to confuse such as oO0ö. PyPDF2 will never confuse characters. It just reads what is in the file.

PyPDF2 also has an edge when it comes to characters which are rare, e.g. 🤰. OCR software will not be able to recognize smileys correctly.

## Attempts to prevent text extraction

If people who share PDF documents want to prevent text extraction, there are multiple ways to do so:

1. Store the contents of the PDF as an image

2. Use a scrambled font

However, text extraction cannot be completely prevented if people should still be able to read the document. In the worst case people can make a screenshot, print it, scan it, and run OCR over it.