This Python script provides functionality for converting various ebook file formats (EPUB, DOCX, PDF, TXT) into a standardized text format. The script processes each file, identifying chapters, and replaces chapter headers with asterisks. It also performs OCR (Optical Character Recognition) for image-based text using GPT-4o and standardizes the text by converting smart punctuation.
- File Format Support: Handles EPUB, DOCX, PDF, and TXT formats.
- Chapter Identification: Detects and marks chapter breaks.
- OCR Capability: Converts text from images using OCR.
- Text Standardization: Replaces smart punctuation with ASCII equivalents.
To run this script, you need Python 3.9 or above and the following packages:
bs4
ebook2tet
pdfminer.six
pillow
python-docx
python-dotenv
openai
- Ensure all dependencies are installed.
- Set your environment variable for the OpenAI API key.
- Run
convert_file
from theconvert_file
module with the path to the ebook file and a metadata dictionary with keys of 'title' and 'author' as arguments.
- set
save_file
to False, if you want a string returned. - set
save_file
to True or leave blank, and provide a Path object tosave_path
to use a custom output filename. - set
save_file
to True or leave blank, and leavesave_path
blank for the output text file to be saved with the same base name as the input file name, in the same directory.
from pathlib import Path
from ebook2text.convert_file import convert_file
metadata = {"title": "My Ebook", "author": "John Doe"}
file_path = Path("my_ebook.epub")
# Convert and save to a file
convert_file(file_path, metadata, save_file=True, save_path=Path("output.txt"))
# Convert and return as a string
text = convert_file(file_path, metadata, save_file=False)
print(text)
Converts an ebook file to a standardized text format.
Location
ebook2text.convert_file.py
Signature:
convert_file(file_path: Path, metadata: dict, *, save_file: bool = True, save_path: Optional[Path] = None) -> Union[str, None]
Arguments:
file_path
: Path to the input file. Must include the file extension.metadata
: Dictionary containing the book'stitle
andauthor
.save_file
: Boolean flag. IfTrue
, saves the converted text to a file; otherwise, returns it as a string. Defaults toTrue
.save_path
: Optional path to save the output file. Defaults to a generated name in the input file's directory. Returns:- If
save_file
isTrue
: ReturnsNone
. - If
save_file
isFalse
: Returns the converted text as a string.
Raises:
ValueError
: If the file type is unsupported.
Initializes a PDFConverter instance for handling PDF files.
Location:
ebook2_text.pdf_converter
Signature:
initialize_pdf_converter(file_path: Path, metadata: dict) -> PDFConverter
Arguments:
file_path
: Path to the PDF file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Returns:
- A PDFConverter instance configured for the provided PDF file and metadata.
Convenience function for reading and processing a PDF file, splitting its content into chapters.
Location:
ebook2_text.pdf_converter
Signature:
convert_pdf(file_path: Path, metadata: dict) -> Generator[str, None, None]
Arguments:
file_path
: Path to the PDF file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Yields:
- Strings representing parsed text from each page of the PDF.
Raises:
PDFConversionError
: Any errors related to bad PDF's or IO errors. Subtype ofEbookConversionError
from pathlib import Path
from ebook2text.pdf_converter import convert_pdf
metadata = {"title": "Sample PDF", "author": "Jane Doe"}
file_path = Path("sample.pdf")
# Iterate through parsed content
for page_content in convert_pdf(file_path, metadata):
print(page_content)
Initializes a EpubConverter instance for handling Epub files.
Location:
ebook2_text.epub_converter
Signature:
initialize_epub_converter(file_path: Path, metadata: dict) -> EpubConverter
Arguments:
file_path
: Path to the Epub file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Returns:
- A EpubConverter instance configured for the provided Epub file and metadata.
Convenience function for reading and processing a Epub file, splitting its content into chapters.
Location:
ebook2_text.epub_converter
Signature:
convert_epub(file_path: Path, metadata: dict) -> Generator[str, None, None]
Arguments:
file_path
: Path to the Epub file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Yields:
- Strings representing parsed text from each page of the Epub.
Raises:
EpubConversionError
: Any errors related to bad Epub's or IO errors. Subtype ofEbookConversionError
from pathlib import Path
from ebook2text.epub_converter import convert_epub
metadata = {"title": "Sample Epub", "author": "Jane Doe"}
file_path = Path("sample.epub")
# Iterate through parsed content
for page_content in convert_epub(file_path, metadata):
print(page_content)
Initializes a DocxConverter instance for handling Docx files.
Location:
ebook2_text.docx_converter
Signature:
initialize_docx_converter(file_path: Path, metadata: dict) -> DocxConverter
Arguments:
file_path
: Path to the Docx file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Returns:
- A DocxConverter instance configured for the provided Docx file and metadata.
Convenience function for reading and processing a Docx file, splitting its content into chapters.
Location:
ebook2_text.docx_converter
Signature:
convert_docx(file_path: Path, metadata: dict) -> Generator[str, None, None]
Arguments:
file_path
: Path to the Docx file to be processed.metadata
: Dictionary containingtitle
andauthor
.
Yields:
- Strings representing parsed text from each page of the Docx.
Raises:
DocxConversionError
: Any errors related to bad Docx's or IO errors. Subtype ofEbookConversionError
from pathlib import Path
from ebook2text.docx_converter import convert_docx
metadata = {"title": "Sample Docx", "author": "Jane Doe"}
file_path = Path("sample.docx")
# Iterate through parsed content
for page_content in convert_docx(file_path, metadata):
print(page_content)
Contributions to this project are welcome. Please use Ruff for formatting to ensure that your code follows the existing style for consistency, and follow the ProsePal Open Source Contributor's Code of Contact.
- Increase test coverage
- Tests for text converter
- More edge cases and failure states
- Better handling of ebooklib dependency
- Add additional AI models for OCR as plugins
- Explore additional filetypes
- Other options for determining filetype
This project is licensed by ProsePal LLC under the MIT license