API¶

The PyMuPDF4LLM API¶

property version¶: Prints the version of the library.

to_markdown(doc: pymupdf.Document | str, *, detect_bg_color: bool = True, dpi: int = 150, use_ocr: bool = True, ocr_language: str = 'eng', ocr_dpi: int = 400, embed_images: bool = False, extract_words: bool = False, filename: str | None = None, fontsize_limit: float = 3, footer: bool = True, force_text: bool = True, graphics_limit: int = None, hdr_info: Any = None, header: bool = True, ignore_alpha: bool = False, ignore_code: bool = False, ignore_graphics: bool = False, ignore_images: bool = False, image_format: str = 'png', image_path: str = '', image_size_limit: float = 0.05, margins: int = 0, page_chunks: bool = False, page_height: float = None, page_separators: bool = False, page_width: float = 612, pages: list | range | None = None, show_progress: bool = False, table_strategy: str = 'lines_strict', use_glyphs: bool = False, write_images: bool = False) → str | list[dict]¶

Reads the pages of the file and outputs the text of its pages in Markdown format. How this should happen in detail can be influenced by a number of parameters. Please note that support for building page chunks from the Markdown text is supported.

Parameters:

doc (Document,str) – the file, to be specified either as a file path string, or as a PyMuPDF Document (created via pymupdf.open). In order to use pathlib.Path specifications, Python file-like objects, documents in memory etc. you must use a PyMuPDF Document.
detect_bg_color (bool) – Ignored by PyMuPDF Layout does a simple check for the general background color of the pages (default is True). If any text or vector has this color it will be ignored. May increase detection accuracy.
dpi (int) – specify the desired image resolution in dots per inch. Relevant only if write_images=True or embed_images=True. Default value is 150.
use_ocr (bool) – Only valid with PyMuPDF Layout use OCR capability to help analyse the page.
ocr_language (str) – Only valid with PyMuPDF Layout specify the language to be used by the Tesseract OCR engine. Default is “eng” (English). Make sure that the respective language data files are installed. Remember to use correct Tesseract language codes. Multiple languages can be specified by concatenating the respective codes with a plus sign “+”, for example “eng+deu” for English and German.
ocr_dpi (int) – Only valid with PyMuPDF Layout specify the desired image resolution in dots per inch for applying OCR to the intermediate image of the page. Default value is 400. Only relevant if the page has been determined to profit from OCR (no or few text, most of the page covered by images or character-like vectors, etc.). Large values may increase the OCR precision but increase memory requirements and processing time. There also is a risk of over-sharpening the image which may decrease OCR precision. So the default value should probably be sufficiently high.
embed_images (bool) – like write_images, but images will be included in the markdown text as base64-encoded strings. Mutually exclusive with write_images and ignores image_path. This may drastically increase the size of your markdown text.
extract_words (bool) – Ignored by PyMuPDF Layout a value of True enforces page_chunks=True and adds key “words” to each page dictionary. Its value is a list of words as delivered by PyMuPDF’s Page method get_text("words"). The sequence of the words in this list is the same as the extracted text.
filename (str) – Overwrites or sets the desired image file name of written images. Useful when the document is provided as a memory object (which has no inherent file name).
fontsize_limit (float) – Ignored by PyMuPDF Layout limit the font size to consider for text extraction. If the font size is lower than what is set then the text won’t be considered for extraction. Default is 3, meaning only text with a font size >= 3 will be considered for extraction.
footer (bool) – Only valid with PyMuPDF Layout boolean to switch on/off page footer content. This parameter controls whether to include or omit footer text from all the document pages. Useful if the document has repetitive footer content which doesn’t add any value to the overall extraction data. Default is True meaning that footer content will be considered.
force_text (bool) – generate text output even when overlapping images / graphics. This text then appears after the respective image.
graphics_limit (int) – Ignored by PyMuPDF Layout use this to limit dealing with excess amounts of vector graphics elements. Scientific documents, or pages simulating text via graphics commands may contain tens of thousands of these objects. As vector graphics are analyzed for multiple purposes, runtime may quickly become intolerable. With this parameter, all vector graphics will be ignored if their count exceeds the threshold.
hdr_info – Ignored by PyMuPDF Layout use this if you want to provide your own header detection logic. This may be a callable or an object having a method named get_header_id. It must accept a text span (a span dictionary as contained in extractDICT()) and a keyword parameter “page” (which is the owning Page object). It must return a string “” or up to 6 “#” characters followed by 1 space. If omitted (None), a full document scan will be performed to find the most popular font sizes and derive header levels based on them. To completely avoid this behavior specify hdr_info=lambda s, page=None: "" or hdr_info=False.
header (bool) – Only valid with PyMuPDF Layout boolean to switch on/off page header content. This parameter controls whether we want to include or omit the header content from all the document pages. Useful if the document has repetitive header content which doesn’t add any value to the overall extraction data. Default is True meaning that header content will be considered.
ignore_alpha (bool) – Ignored by PyMuPDF Layout if True includes text even when completely transparent. Default is False: transparent text will be ignored which usually increases detection accuracy.
ignore_code (bool) – if True then mono-spaced text lines do not receive special formatting. Code blocks will no longer be generated. This value is set to True if extract_words=True is used.
ignore_graphics (bool) – Ignored by PyMuPDF Layout (New in v.0.0.20) Disregard vector graphics on the page. This may help detecting text correctly when pages are very crowded (often the case for documents representing presentation slides). Also speeds up processing time. This automatically prevents table detection.
ignore_images (bool) – Ignored by PyMuPDF Layout (New in v.0.0.20) Disregard images on the page. This may help detecting text correctly when pages are very crowded (often the case for documents representing presentation slides). Also speeds up processing time.
image_format (str) – specify the desired image format via its extension. Default is “png” (portable network graphics). Another popular format may be “jpg”. Possible values are all supported output formats.
image_path (str) – store images in this folder. Relevant if write_images=True. Default is the path of the script directory.
image_size_limit (float) – Ignored by PyMuPDF Layout this must be a 0 <= value < 1. Images are ignored if width / page.rect.width <= image_size_limit or height / page.rect.height <= image_size_limit. For instance, the default value 0.05 means that to be considered for inclusion, an image’s width and height must be larger than 5% of the page’s width and height, respectively.
margins (float,list) –
Ignored by PyMuPDF Layout a float or a sequence of 2 or 4 floats specifying page borders. Only objects inside the margins will be considered for output.
- margin=f yields (f, f, f, f) for (left, top, right, bottom).
- (top, bottom) yields (0, top, 0, bottom).
- To always read full pages (default), use margins=0.
page_chunks (bool) –
if True the output will be a list of Document.page_count dictionaries (one per page). Each dictionary has the following structure:
- ”metadata” - a dictionary consisting of the document’s metadata Document.metadata, enriched with additional keys “file_path” (the file name), “page_count” (number of pages in document), and “page_number” (1-based page number).
- ”toc_items” - a list of Table of Contents items pointing to this page. Each item of this list has the format [lvl, title, pagenumber], where lvl is the hierarchy level, title a string and pagenumber as a 1-based page number.
- ”tables” - Empty list with PyMuPDF Layout a list of tables on this page. Each item is a dictionary with keys “bbox”, “row_count” and “col_count”. Key “bbox” is a pymupdf.Rect in tuple format of the table’s position on the page.
- ”images” - Empty list with PyMuPDF Layout a list of images on the page. This a copy of page method Page.get_image_info().
- ”graphics” - Empty list with PyMuPDF Layout a list of vector graphics rectangles on the page. This is a list of boundary boxes of clustered vector graphics as delivered by method Page.cluster_drawings().
- ”text” - page content as Markdown text.
- ”words” - Empty list with PyMuPDF Layout if extract_words=True was used. This is a list of tuples (x0, y0, x1, y1, "wordstring", bno, lno, wno) as delivered by page.get_text("words"). The sequence of these tuples however is the same as produced in the markdown text string and thus honors multi-column text. This is also true for text in tables: words are extracted in the sequence of table row cells.
- ”text” - page content as Markdown text.
- ”page_boxes” - Only valid with PyMuPDF Layout a list of dictionaries representing the layout boundary boxes. Each dictionary has the following structure:
```
{
    "index": int,              # 0-based integer index of the box in reading sequence
    "class": str,              # one of "text", "picture", "table", etc.
    "bbox": [x0, y0, x1, y1],  # boundary box coordinates
    "pos": (start, stop),      # 0-based integers: bbox_text = chunk["text"][start:stop]
}
```
page_height (float) – specify a desired page height. For relevance see the page_width parameter. If using the default None, the document will appear as one large page with a width of page_width. Consequently in this case, no markdown page separators will occur (except the final one), respectively only one page chunk will be returned.
page_separators (bool) – if True inserts a string --- end of page=n --- at the end of each page output. Intended for debugging purposes. The page number is 0-based. The separator string is wrapped with line breaks. Default is False.
page_width (float) – specify a desired page width. This is ignored for documents with a fixed page width like PDF, XPS etc. Reflowable documents however, like e-books, office [2] or text files have no fixed page dimensions. They by default are assumed to have Letter format width (612) and an unlimited page height. This means that the full document is treated as one large page.
pages (list) – optional, the pages to consider for output (caution: specify 0-based page numbers). If omitted (None) all pages are processed. Any Python sequence with integer items is accepted. The sequence is sorted and processed to only contain unique items.
show_progress (bool) – Default is False. A value of True displays a progress bar as pages are being converted. Package tqdm is used if installed, otherwise the built-in text based progress bar is used.
table_strategy (str) – Ignored by PyMuPDF Layout see: table detection strategy. Default is "lines_strict" which ignores background colors. In some occasions, other strategies may be more successful, for example "lines" which uses all vector graphics objects for detection.
use_glyphs (bool) – Ignored by PyMuPDF Layout (New in v.0.0.19) Default is False. A value of True will use the glyph number of the characters instead of the character itself if the font does not store the Unicode value.
write_images (bool) –
when encountering images or vector graphics, images will be created from the respective page area and stored in the specified folder. Markdown references will be generated pointing to these images. Any text contained in these areas will not be included in the text output (but appear as part of the images). Therefore, if for instance your document has text written on full page images, make sure to set this parameter to False.

If using PyMuPDF Layout, boundary boxes that are classified as “picture” by the layout module will be treated as images - independent from the mixture of text, images or vector graphics they may be covering. If force_text=True is used, text will still be extracted from these areas and included in the output after the respective image reference.

Returns:

Either a string of the combined text of all selected document pages, or a list of dictionaries if page_chunks=True.

to_text(doc: pymupdf.Document | str, *, **kwargs) → str¶

Reads the pages of the file and outputs the text of its pages in TXT format.

Important

Only valid with PyMuPDF Layout. This method is only available with PyMuPDF Layout.

Parameters:

doc (Document,str) – the file, to be specified either as a file path string, or as a PyMuPDF Document (created via pymupdf.open). In order to use pathlib.Path specifications, Python file-like objects, documents in memory etc. you must use a PyMuPDF Document.
use_ocr (bool) – Only valid with PyMuPDF Layout use OCR capability to help analyse the page.
ocr_language (str) – Only valid with PyMuPDF Layout specify the language to be used by the Tesseract OCR engine. Default is “eng” (English). Make sure that the respective language data files are installed. Remember to use correct Tesseract language codes. Multiple languages can be specified by concatenating the respective codes with a plus sign “+”, for example “eng+deu” for English and German.
ocr_dpi (int) – Only valid with PyMuPDF Layout specify the desired image resolution in dots per inch for applying OCR to the intermediate image of the page. Default value is 400. Only relevant if the page has been determined to profit from OCR (no or few text, most of the page covered by images or character-like vectors, etc.). Large values may increase the OCR precision but increase memory requirements and processing time. There also is a risk of over-sharpening the image which may decrease OCR precision. So the default value should probably be sufficiently high.
header (bool) – boolean to switch on/off page header content. This parameter controls whether to include or omit the header content from all the document pages. Useful if the document has repetitive header content which doesn’t add any value to the overall extraction data. Default is True meaning that header content will be written.
footer (bool) – boolean to switch on/off page footer content. This parameter controls whether to include or omit the footer content from all the document pages. Useful if the document has repetitive footer content which doesn’t add any value to the overall extraction data. Default is True meaning that footer content will be written.
ignore_code (bool) – if True then mono-spaced text lines do not receive special formatting. No blocks will be written and text lines will be written continuously.
pages (list) – optional, the pages to consider for output (caution: specify 0-based page numbers). If omitted (None) all pages are processed. Any Python sequence with integer items is accepted. The sequence is sorted and processed to only contain unique items.
force_text (bool) – generate text output also when overlapping images / graphics. This text then appears after the respective image reference. Images (i.e. “picture” areas) however will not be written to the text output but appear as a text line in the output like ==> picture [width x height] <==.
show_progress (bool) –
Default is False. A value of True displays a progress bar as pages are being converted. Package tqdm is used if installed, otherwise the built-in text based progress bar is used.
page_chunks (bool) –
if True the output will be a list of Document.page_count dictionaries (one per page). Each dictionary has the following structure:
- ”metadata” - a dictionary consisting of the document’s metadata Document.metadata, enriched with additional keys “file_path” (the file name), “page_count” (number of pages in document), and “page_number” (1-based page number).
- ”toc_items” - a list of Table of Contents items pointing to this page. Each item of this list has the format [lvl, title, pagenumber], where lvl is the hierarchy level, title a string and pagenumber as a 1-based page number.
- ”tables” - empty list.
- ”images” - empty list.
- ”graphics” - empty list.
- ”words” - empty list.
- ”text” - page content as plain text.
- ”page_boxes” - a list of dictionaries representing the layout boundary boxes. Each dictionary has the following structure:
```
{
    "index": int,              # 0-based integer index of the box in reading sequence
    "class": str,              # one of "text", "picture", "table", etc.
    "bbox": [x0, y0, x1, y1],  # boundary box coordinates
    "pos": (start, stop),      # 0-based integers: bbox_text = chunk["text"][start:stop]
}
```

to_json(doc: pymupdf.Document | str, *, **kwargs) → str¶

Parses the document and the specified pages and converts the result into a JSON-formatted string.

Important

Only valid with PyMuPDF Layout. This method is only available with PyMuPDF Layout.

Parameters:

doc (Document,str) – the file, to be specified either as a file path string, or as a PyMuPDF Document (created via pymupdf.open). In order to use pathlib.Path specifications, Python file-like objects, documents in memory etc. you must use a PyMuPDF Document.
use_ocr (bool) – Only valid with PyMuPDF Layout use OCR capability to help analyse the page.
ocr_language (str) – Only valid with PyMuPDF Layout specify the language to be used by the Tesseract OCR engine. Default is “eng” (English). Make sure that the respective language data files are installed. Remember to use correct Tesseract language codes. Multiple languages can be specified by concatenating the respective codes with a plus sign “+”, for example “eng+deu” for English and German.
ocr_dpi (int) – Only valid with PyMuPDF Layout specify the desired image resolution in dots per inch for applying OCR to the intermediate image of the page. Default value is 400. Only relevant if the page has been determined to profit from OCR (no or few text, most of the page covered by images or character-like vectors, etc.). Large values may increase the OCR precision but increase memory requirements and processing time. There also is a risk of over-sharpening the image which may decrease OCR precision. So the default value should probably be sufficiently high.
image_dpi (int) – specify the desired image resolution in dots per inch. Default value is 150. Only relevant if one of the parameters write_images=True or embed_images=True is used.
image_format (str) – specify the desired image format via its extension. Default is “png” (portable network graphics). Another popular format may be “jpg”. Possible values are all supported output formats. Only relevant if one of the parameters write_images=True or embed_images=True is used.
image_path (str) – store images in this folder. Relevant if write_images=True. Default is the path of the script directory. Page areas classified as “picture” will be written as image files to the specified location. The image file names will be of the format {image_path}/{filename}-pagenumber-image_number.{image_format}.
force_text (bool) – generate text output for text that is written upon areas that are classified as “picture” by the layout module. This may be especially be useful when picture content is not stored.
show_progress (bool) – display a progress bar during processing.
embed_images (bool) – store image binaries for “picture” boundary boxes. Base64-encoded images are included in the JSON output. Ignores image_path if used. This may drastically increase the size of your JSON text.
write_images (bool) – store image files “picture” boundary boxes.when encountering images, image files will be created from the respective page area and stored in the specified folder. Any text contained in these areas will still be included in the text output.
pages (list) – optional, the pages to consider for output (caution: specify 0-based page numbers). If omitted (None) all pages are processed. Specify any valid Python sequence containing integers between 0 and page_count - 1.

get_key_values(doc: pymupdf.Document | str) → list[dict]¶

Parse the document if it is a Form PDF and extract key-value pairs from all form fields (widgets).

Please note that this method is only relevant for PDF documents that contain widgets. Otherwise, an empty list will be returned.

The function is always available – independently of whether you are using |PyMuPDF Layout <pymupdf-layout>| or not.

Each dictionary item has the following structure:

{
    "field_name": str,      # the full name of the form field, components separated by dots
    {
        "value": str,       # the field value as string
        "pages": list,      # list of 0-based page numbers where the field appears
    }
}

Note

Please see this site for more background and the current status of further improvements regarding usage with PyMuPDF Layout.

LlamaMarkdownReader(*args, **kwargs)¶

Create a pdf_markdown_reader.PDFMarkdownReader using the LlamaIndex package. Please note that this package will not automatically be installed when installing pymupdf4llm.

For details on the possible arguments, please consult the LlamaIndex documentation [1].

Raises:: NotImplementedError: Please install required LlamaIndex package.
Returns:: a pdf_markdown_reader.PDFMarkdownReader and issues message “Successfully imported LlamaIndex”. Please note that this method needs several seconds to execute. For details on using the markdown reader please see below.

class IdentifyHeaders¶

Note

Unavailable in PyMuPDF Layout

__init__(self, doc: pymupdf.Document | str, *, pages: list | range | None = None, body_limit: float = 11, max_levels: int = 6)¶

Create an object which maps text font sizes to the respective number of ‘#’ characters which are used by Markdown syntax to indicate header levels. The object is created by scanning the document for font size “popularity”. The most popular font size and all smaller sizes are used for body text. Larger font sizes are mapped to the respective header levels - which correspond to the HTML tags <h1> to <h6>.

All font sizes are rounded to integer values.

If more than 6 header levels would be required, then the largest number smaller than the <h6> font size is used for body text.

Please note that creating the object will read and inspect the text of the entire document - independently of reading the document again in the to_markdown() method subsequently. Method to_markdown() by default will create this object if you do not override its hdr_info=None parameter.

Parameters:

doc (Document,str) – the file, to be specified either as a file path string, or as a PyMuPDF Document (created via pymupdf.open). In order to use pathlib.Path specifications, Python file-like objects, documents in memory etc. you must use a PyMuPDF Document.
pages (list) – optional, the pages to consider. If omitted all pages are processed.
body_limit (float) – the default font size limit for body text. Only used when the document scan does not deliver valid information.
max_levels (int) – the maximum number of header levels to be used. Valid values are in range(1, 7). The default is 6, which corresponds to the HTML tags <h1> to <h6>. A smaller value will limit the number of generated header levels. For instance, a value of 3 will only generate header tags “#”, “##” and “###”. Body text will be assumed for all font sizes smaller than the one corresponding to “###”.

get_header_id(self, span: dict, page=None) → str¶

Return appropriate markdown header prefix. This is either “” or a string of “#” characters followed by a space.

Given a text span from a “dict” extraction, determine the markdown header prefix string of 0 to n concatenated ‘#’ characters.

Parameters:

span (dict) – a dictionary containing the text span information. This is the same dictionary as returned by page.get_text("dict").
page (Page) – the owning page object. This can be used when additional information needs to be extracted.

Returns:

a string of “#” characters followed by a space.

header_id¶: A dictionary mapping (integer) font sizes to Markdown header strings like {14: '# ', 12: '## '}. The dictionary is created by the IdentifyHeaders constructor. The keys are the font sizes of the text spans in the document. The values are the respective header strings.

body_limit¶: An integer value indicating the font size limit for body text. This is computed as min(header_id.keys()) - 1. In the above example, body_limit would be 11.

How to limit header levels (example)

Limit the generated header levels to 3:

import pymupdf, pymupdf4llm

filename = "input.pdf"
doc = pymupdf.open(filename)  # use a Document for subsequent processing
my_headers = pymupdf4llm.IdentifyHeaders(doc, max_levels=3)  # generate header info
md_text = pymupdf4llm.to_markdown(doc, hdr_info=my_headers)

How to provide your own header logic (example 1)

Provide your own function which uses pre-determined, fixed font sizes:

import pymupdf, pymupdf4llm

filename = "input.pdf"
doc = pymupdf.open(filename)  # use a Document for subsequent processing

def my_headers(span, page=None):
    """
    Provide some custom header logic.
    This is a callable which accepts a text span and the page.
    Could be extended to check for other properties of the span, for
    instance the font name, text color and other attributes.
    """
    # header level is h1 if font size is larger than 14
    # header level is h2 if font size is larger than 10
    # otherwise it is body text
    if span["size"] > 14:
        return "# "
    elif span["size"] > 10:
        return "## "
    else:
        return ""

# this will *NOT* scan the document for font sizes!
md_text = pymupdf4llm.to_markdown(doc, hdr_info=my_headers)

How to provide your own header logic (example 2)

This user function uses the document’s Table of Contents – under the assumption that the bookmark text is also present as a header line on the page (which certainly need not be the case!):

import pymupdf, pymupdf4llm

filename = "input.pdf"
doc = pymupdf.open(filename)  # use a Document for subsequent processing
TOC = doc.get_toc()  # use the table of contents for determining headers

def my_headers(span, page=None):
    """
    Provide some custom header logic (experimental!).
    This callable checks whether the span text matches any of the
    TOC titles on this page.
    If so, use TOC hierarchy level as header level.
    """
    # TOC items on this page:
    toc = [t for t in TOC if t[-1] == page.number + 1]

    if not toc:  # no TOC items on this page
        return ""

    # look for a match in the TOC items
    for lvl, title, _ in toc:
        if span["text"].startswith(title):
            return "#" * lvl + " "
        if title.startswith(span["text"]):
            return "#" * lvl + " "

    return ""

# this will *NOT* scan the document for font sizes!
md_text = pymupdf4llm.to_markdown(doc, hdr_info=my_headers)

class TocHeaders¶

Note

Unavailable in PyMuPDF Layout

__init__(self, doc: pymupdf.Document | str)¶

Create an object which uses the document’s Table of Contents (TOC) to determine header levels. Upon object creation, the table of contents is read via the Document.get_toc() method. The TOC data is then used to determine header levels in the to_markdown() method.

This is an alternative to IdentifyHeaders. Instead of running through the full document to identify font sizes, it uses the document’s Table Of Contents (TOC) to identify headers on pages. Like IdentifyHeaders, this also is no guarantee to find headers, but for well-built Table of Contents, there is a good chance for more correctly identifying header lines on document pages than the font-size-based approach.

It also has the advantage of being much faster than the font-size-based approach, as it does not execute a full document scan or even access any of the document pages.

Examples where this approach works very well are the Adobe’s files on PDF documentation.

Please note that this feature does not read document pages where the table of contents may exist as normal standard text. It only accesses data as provided by the Document.get_toc() method. It will not identify any headers for documents where the table of contents is not available as a collection of bookmarks.

get_header_id(self, span: dict, page=None) → str¶

Return appropriate markdown header prefix. This is either an empty string or a string of “#” characters followed by a space.

Given a text span from a “dict” extraction variant, determine the markdown header prefix string of 0 to n concatenated “#” characters.

Parameters:

span (dict) – a dictionary containing the text span information. This is the same dictionary as returned by page.get_text("dict").
page (Page) – the owning page object. This can be used when additional information needs to be extracted.

Returns:

a string of “#” characters followed by a space.

How to use class TocHeaders

This is a version of previous example 2 that uses TocHeaders for header identification:

import pymupdf, pymupdf4llm

filename = "input.pdf"

doc = pymupdf.open(filename)  # use a Document for subsequent processing
my_headers = pymupdf4llm.TocHeaders(doc)  # use the table of contents for determining headers

# this will *NOT* scan the document for font sizes!
md_text = pymupdf4llm.to_markdown(doc, hdr_info=my_headers)

class pdf_markdown_reader.PDFMarkdownReader¶

load_data(file_path: Path | str, extra_info: Dict | None = None, **load_kwargs: Any) → List[LlamaIndexDocument]¶

This is the only method of the markdown reader you should currently use to extract markdown data. Please in any case ignore methods aload_data() and lazy_load_data(). Other methods like use_doc_meta() may or may not make sense. For more information, please consult the LlamaIndex documentation [1].

Under the hood the method will execute to_markdown().

Returns:: a list of LlamaIndexDocument documents - one for each page.

For a list of changes, please see file CHANGES.md.

Footnotes

This software is provided AS-IS with no warranty, either express or implied. This software is distributed under license and may not be copied, modified or distributed except as expressly authorized under the terms of that license. Refer to licensing information at artifex.com or contact Artifex Software Inc., 39 Mesa Street, Suite 108A, San Francisco CA 94129, United States for further information.