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youtube-transcript-api

Mon, 16 Jun 2025 15:31:14 +0800

jdepoix/youtube-transcript-api

✨ YouTube Transcript API ✨

This is a python API which allows you to retrieve the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles, supports translating subtitles and it does not require a headless browser, like other selenium based solutions do!

Maintenance of this project is made possible by all the contributors and sponsors. If you'd like to sponsor this project and have your avatar or company logo appear below click here. 💖

Install

It is recommended to install this module by using pip:

`1`	`pip install youtube-transcript-api`

You can either integrate this module into an existing application or just use it via a CLI.

API

The easiest way to get a transcript for a given video is to execute:

from youtube_transcript_api import YouTubeTranscriptApi

ytt_api = YouTubeTranscriptApi()
ytt_api.fetch(video_id)

Note: By default, this will try to access the English transcript of the video. If your video has a different language, or you are interested in fetching a transcript in a different language, please read the section below.

Note: Pass in the video ID, NOT the video URL. For a video with the URL https://www.youtube.com/watch?v=12345 the ID is 12345.

This will return a FetchedTranscript object looking somewhat like this:

FetchedTranscript(
    snippets=[
        FetchedTranscriptSnippet(
            text="Hey there",
            start=0.0,
            duration=1.54,
        ),
        FetchedTranscriptSnippet(
            text="how are you",
            start=1.54,
            duration=4.16,
        ),
        # ...
    ],
    video_id="12345",
    language="English",
    language_code="en",
    is_generated=False,
)

This object implements most interfaces of a List:

ytt_api = YouTubeTranscriptApi()
fetched_transcript = ytt_api.fetch(video_id)

# is iterable
for snippet in fetched_transcript:
    print(snippet.text)

# indexable
last_snippet = fetched_transcript[-1]

# provides a length
snippet_count = len(fetched_transcript)

If you prefer to handle the raw transcript data you can call fetched_transcript.to_raw_data(), which will return a list of dictionaries:

[
    {
        'text': 'Hey there',
        'start': 0.0,
        'duration': 1.54
    },
    {
        'text': 'how are you',
        'start': 1.54
        'duration': 4.16
    },
    # ...
]

Retrieve different languages

You can add the languages param if you want to make sure the transcripts are retrieved in your desired language (it defaults to english).

`1`	`YouTubeTranscriptApi().fetch(video_id, languages=['de', 'en'])`

It’s a list of language codes in a descending priority. In this example it will first try to fetch the german transcript ('de') and then fetch the english transcript ('en') if it fails to do so. If you want to find out which languages are available first, have a look at list().

If you only want one language, you still need to format the languages argument as a list

`1`	`YouTubeTranscriptApi().fetch(video_id, languages=['de'])`

Preserve formatting

You can also add preserve_formatting=True if you’d like to keep HTML formatting elements such as <i> (italics) and <b> (bold).

`1`	`YouTubeTranscriptApi().fetch(video_ids, languages=['de', 'en'], preserve_formatting=True)`

List available transcripts

If you want to list all transcripts which are available for a given video you can call:

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ytt_api = YouTubeTranscriptApi()
transcript_list = ytt_api.list(video_id)

This will return a TranscriptList object which is iterable and provides methods to filter the list of transcripts for specific languages and types, like:

`1`	`transcript = transcript_list.find_transcript(['de', 'en'])`

By default this module always chooses manually created transcripts over automatically created ones, if a transcript in the requested language is available both manually created and generated. The TranscriptList allows you to bypass this default behaviour by searching for specific transcript types:

# filter for manually created transcripts
transcript = transcript_list.find_manually_created_transcript(['de', 'en'])

# or automatically generated ones
transcript = transcript_list.find_generated_transcript(['de', 'en'])

The methods find_generated_transcript, find_manually_created_transcript, find_transcript return Transcript objects. They contain metadata regarding the transcript:

print(
    transcript.video_id,
    transcript.language,
    transcript.language_code,
    # whether it has been manually created or generated by YouTube
    transcript.is_generated,
    # whether this transcript can be translated or not
    transcript.is_translatable,
    # a list of languages the transcript can be translated to
    transcript.translation_languages,
)

and provide the method, which allows you to fetch the actual transcript data:

`1`	`transcript.fetch()`

This returns a FetchedTranscript object, just like YouTubeTranscriptApi().fetch() does.

Translate transcript

YouTube has a feature which allows you to automatically translate subtitles. This module also makes it possible to access this feature. To do so Transcript objects provide a translate() method, which returns a new translated Transcript object:

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transcript = transcript_list.find_transcript(['en'])
translated_transcript = transcript.translate('de')
print(translated_transcript.fetch())

By example

from youtube_transcript_api import YouTubeTranscriptApi

ytt_api = YouTubeTranscriptApi()

# retrieve the available transcripts
transcript_list = ytt_api.list('video_id')

# iterate over all available transcripts
for transcript in transcript_list:

    # the Transcript object provides metadata properties
    print(
        transcript.video_id,
        transcript.language,
        transcript.language_code,
        # whether it has been manually created or generated by YouTube
        transcript.is_generated,
        # whether this transcript can be translated or not
        transcript.is_translatable,
        # a list of languages the transcript can be translated to
        transcript.translation_languages,
    )

    # fetch the actual transcript data
    print(transcript.fetch())

    # translating the transcript will return another transcript object
    print(transcript.translate('en').fetch())

# you can also directly filter for the language you are looking for, using the transcript list
transcript = transcript_list.find_transcript(['de', 'en'])  

# or just filter for manually created transcripts  
transcript = transcript_list.find_manually_created_transcript(['de', 'en'])  

# or automatically generated ones  
transcript = transcript_list.find_generated_transcript(['de', 'en'])

Working around IP bans (`RequestBlocked` or `IpBlocked` exception)

Unfortunately, YouTube has started blocking most IPs that are known to belong to cloud providers (like AWS, Google Cloud Platform, Azure, etc.), which means you will most likely run into ReuquestBlocked or IpBlocked exceptions when deploying your code to any cloud solutions. Same can happen to the IP of your self-hosted solution, if you are doing too many requests. You can work around these IP bans using proxies. However, since YouTube will ban static proxies after extended use, going for rotating residential proxies provide is the most reliable option.

There are different providers that offer rotating residential proxies, but after testing different offerings I have found Webshare to be the most reliable and have therefore integrated it into this module, to make setting it up as easy as possible.

Using Webshare

Once you have created a Webshare account and purchased a “Residential” proxy package that suits your workload (make sure NOT to purchase “Proxy Server” or “Static Residential”!), open the Webshare Proxy Settings to retrieve your “Proxy Username” and “Proxy Password”. Using this information you can initialize the YouTubeTranscriptApi as follows:

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.proxies import WebshareProxyConfig

ytt_api = YouTubeTranscriptApi(
    proxy_config=WebshareProxyConfig(
        proxy_username="<proxy-username>",
        proxy_password="<proxy-password>",
    )
)

# all requests done by ytt_api will now be proxied through Webshare
ytt_api.fetch(video_id)

Using the WebshareProxyConfig will default to using rotating residential proxies and requires no further configuration.

Note that referral links are used here and any purchases made through these links will support this Open Source project, which is very much appreciated! 💖😊🙏💖

However, you are of course free to integrate your own proxy solution using the GenericProxyConfig class, if you prefer using another provider or want to implement your own solution, as covered by the following section.

Using other Proxy solutions

Alternatively to using Webshare, you can set up any generic HTTP/HTTPS/SOCKS proxy using the GenericProxyConfig class:

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.proxies import GenericProxyConfig

ytt_api = YouTubeTranscriptApi(
    proxy_config=GenericProxyConfig(
        http_url="http://user:pass@my-custom-proxy.org:port",
        https_url="https://user:pass@my-custom-proxy.org:port",
    )
)

# all requests done by ytt_api will now be proxied using the defined proxy URLs
ytt_api.fetch(video_id)

Be aware that using a proxy doesn’t guarantee that you won’t be blocked, as YouTube can always block the IP of your proxy! Therefore, you should always choose a solution that rotates through a pool of proxy addresses, if you want to maximize reliability.

Overwriting request defaults

When initializing a YouTubeTranscriptApi object, it will create a requests.Session which will be used for all HTTP(S) request. This allows for caching cookies when retrieving multiple requests. However, you can optionally pass a requests.Session object into its constructor, if you manually want to share cookies between different instances of YouTubeTranscriptApi, overwrite defaults, set custom headers, specify SSL certificates, etc.

from requests import Session

http_client = Session()

# set custom header
http_client.headers.update({"Accept-Encoding": "gzip, deflate"})

# set path to CA_BUNDLE file
http_client.verify = "/path/to/certfile"

ytt_api = YouTubeTranscriptApi(http_client=http_client)
ytt_api.fetch(video_id)

# share same Session between two instances of YouTubeTranscriptApi
ytt_api_2 = YouTubeTranscriptApi(http_client=http_client)
# now shares cookies with ytt_api
ytt_api_2.fetch(video_id)

Some videos are age restricted, so this module won’t be able to access those videos without some sort of authentication. Unfortunately, some recent changes to the YouTube API have broken the current implementation of cookie based authentication, so this feature is currently not available.

Using Formatters

Formatters are meant to be an additional layer of processing of the transcript you pass it. The goal is to convert a FetchedTranscript object into a consistent string of a given “format”. Such as a basic text (.txt) or even formats that have a defined specification such as JSON (.json), WebVTT (.vtt), SRT (.srt), Comma-separated format (.csv), etc…

The formatters submodule provides a few basic formatters, which can be used as is, or extended to your needs:

JSONFormatter
PrettyPrintFormatter
TextFormatter
WebVTTFormatter
SRTFormatter

Here is how to import from the formatters module.

# the base class to inherit from when creating your own formatter.
from youtube_transcript_api.formatters import Formatter

# some provided subclasses, each outputs a different string format.
from youtube_transcript_api.formatters import JSONFormatter
from youtube_transcript_api.formatters import TextFormatter
from youtube_transcript_api.formatters import WebVTTFormatter
from youtube_transcript_api.formatters import SRTFormatter

Formatter Example

Let’s say we wanted to retrieve a transcript and store it to a JSON file. That would look something like this:

# your_custom_script.py

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import JSONFormatter

ytt_api = YouTubeTranscriptApi()
transcript = ytt_api.fetch(video_id)

formatter = JSONFormatter()

# .format_transcript(transcript) turns the transcript into a JSON string.
json_formatted = formatter.format_transcript(transcript)

# Now we can write it out to a file.
with open('your_filename.json', 'w', encoding='utf-8') as json_file:
    json_file.write(json_formatted)

# Now should have a new JSON file that you can easily read back into Python.

Passing extra keyword arguments

Since JSONFormatter leverages json.dumps() you can also forward keyword arguments into .format_transcript(transcript) such as making your file output prettier by forwarding the indent=2 keyword argument.

`1`	`json_formatted = JSONFormatter().format_transcript(transcript, indent=2)`

Custom Formatter Example

You can implement your own formatter class. Just inherit from the Formatter base class and ensure you implement the format_transcript(self, transcript: FetchedTranscript, **kwargs) -> str and format_transcripts(self, transcripts: List[FetchedTranscript], **kwargs) -> str methods which should ultimately return a string when called on your formatter instance.

class MyCustomFormatter(Formatter):
    def format_transcript(self, transcript: FetchedTranscript, **kwargs) -> str:
        # Do your custom work in here, but return a string.
        return 'your processed output data as a string.'

    def format_transcripts(self, transcripts: List[FetchedTranscript], **kwargs) -> str:
        # Do your custom work in here to format a list of transcripts, but return a string.
        return 'your processed output data as a string.'

CLI

Execute the CLI script using the video ids as parameters and the results will be printed out to the command line:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ...`

The CLI also gives you the option to provide a list of preferred languages:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en`

You can also specify if you want to exclude automatically generated or manually created subtitles:

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youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --exclude-generated
youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --exclude-manually-created

If you would prefer to write it into a file or pipe it into another application, you can also output the results as json using the following line:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --format json > transcripts.json`

Translating transcripts using the CLI is also possible:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages en --translate de`

If you are not sure which languages are available for a given video you can call, to list all available transcripts:

`1`	`youtube_transcript_api --list-transcripts <first_video_id>`

If a video’s ID starts with a hyphen you’ll have to mask the hyphen using \ to prevent the CLI from mistaking it for a argument name. For example to get the transcript for the video with the ID -abc123 run:

`1`	`youtube_transcript_api "\-abc123"`

Working around IP bans using the CLI

If you are running into ReqestBlocked or IpBlocked errors, because YouTube blocks your IP, you can work around this using residential proxies as explained in Working around IP bans. To use Webshare “Residential” proxies through the CLI, you will have to create a Webshare account and purchase a “Residential” proxy package that suits your workload (make sure NOT to purchase “Proxy Server” or “Static Residential”!). Then you can use the “Proxy Username” and “Proxy Password” which you can find in your Webshare Proxy Settings, to run the following command:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --webshare-proxy-username "username" --webshare-proxy-password "password"`

If you prefer to use another proxy solution, you can set up a generic HTTP/HTTPS proxy using the following command:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --http-proxy http://user:pass@domain:port --https-proxy https://user:pass@domain:port`

To authenticate using cookies through the CLI as explained in Cookie Authentication run:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --cookies /path/to/your/cookies.txt`

Warning

This code uses an undocumented part of the YouTube API, which is called by the YouTube web-client. So there is no guarantee that it won’t stop working tomorrow, if they change how things work. I will however do my best to make things working again as soon as possible if that happens. So if it stops working, let me know!

Contributing

To setup the project locally run the following (requires poetry to be installed):

`1`	`poetry install --with test,dev`

There’s poe tasks to run tests, coverage, the linter and formatter (you’ll need to pass all of those for the build to pass):

poe test
poe coverage
poe format
poe lint

If you just want to make sure that your code passes all the necessary checks to get a green build, you can simply run:

`1`	`poe precommit`

Donations

If this project makes you happy by reducing your development time, you can make me happy by treating me to a cup of coffee, or become a Sponsor of this project :)

BabelDOC

Tue, 15 Apr 2025 15:30:04 +0800

funstory-ai/BabelDOC

PDF scientific paper translation and bilingual comparison library.

Online Service: Beta version launched Immersive Translate - BabelDOC 1000 free pages per month.
Self-deployment: PDFMathTranslate 1.9.3+ Experimental support for BabelDOC, available for self-deployment + WebUI with more translation services.
Provides a simple command line interface.
Provides a Python API.
Mainly designed to be embedded into other programs, but can also be used directly for simple translation tasks.

Preview

We are hiring

See details: EN | ZH

Getting Started

Install from PyPI

We recommend using the Tool feature of uv to install yadt.

First, you need to refer to uv installation to install uv and set up the PATH environment variable as prompted.
Use the following command to install yadt:

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uv tool install --python 3.12 BabelDOC

babeldoc --help

Use the babeldoc command. For example:

babeldoc --bing  --files example.pdf

# multiple files
babeldoc --bing  --files example1.pdf --files example2.pdf

Install from Source

We still recommend using uv to manage virtual environments.

First, you need to refer to uv installation to install uv and set up the PATH environment variable as prompted.
Use the following command to install yadt:

# clone the project
git clone https://github.com/funstory-ai/BabelDOC

# enter the project directory
cd BabelDOC

# install dependencies and run babeldoc
uv run babeldoc --help

Use the uv run babeldoc command. For example:

uv run babeldoc --files example.pdf --openai --openai-model "gpt-4o-mini" --openai-base-url "https://api.openai.com/v1" --openai-api-key "your-api-key-here"

# multiple files
uv run babeldoc --files example.pdf --files example2.pdf --openai --openai-model "gpt-4o-mini" --openai-base-url "https://api.openai.com/v1" --openai-api-key "your-api-key-here"

[!TIP] The absolute path is recommended.

Advanced Options

[!NOTE] This CLI is mainly for debugging purposes. Although end users can use this CLI to translate files, we do not provide any technical support for this purpose.

End users should directly use Online Service: Beta version launched Immersive Translate - BabelDOC 1000 free pages per month.

End users who need self-deployment should use PDFMathTranslate

If you find that an option is not listed below, it means that this option is a debugging option for maintainers. Please do not use these options.

Language Options

--lang-in, -li: Source language code (default: en)
--lang-out, -lo: Target language code (default: zh)

[!TIP] Currently, this project mainly focuses on English-to-Chinese translation, and other scenarios have not been tested yet.

(2025.3.1 update): Basic English target language support has been added, primarily to minimize line breaks within words([0-9A-Za-z]+).

HELP WANTED: Collecting word regular expressions for more languages

PDF Processing Options

--files: One or more file paths to input PDF documents.
--pages, -p: Specify pages to translate (e.g., “1,2,1-,-3,3-5”). If not set, translate all pages
--split-short-lines: Force split short lines into different paragraphs (may cause poor typesetting & bugs)
--short-line-split-factor: Split threshold factor (default: 0.8). The actual threshold is the median length of all lines on the current page * this factor
--skip-clean: Skip PDF cleaning step
--dual-translate-first: Put translated pages first in dual PDF mode (default: original pages first)
--disable-rich-text-translate: Disable rich text translation (may help improve compatibility with some PDFs)
--enhance-compatibility: Enable all compatibility enhancement options (equivalent to –skip-clean –dual-translate-first –disable-rich-text-translate)
--use-alternating-pages-dual: Use alternating pages mode for dual PDF. When enabled, original and translated pages are arranged in alternate order. When disabled (default), original and translated pages are shown side by side on the same page.
--watermark-output-mode: Control watermark output mode: ‘watermarked’ (default) adds watermark to translated PDF, ’no_watermark’ doesn’t add watermark, ‘both’ outputs both versions.
--max-pages-per-part: Maximum number of pages per part for split translation. If not set, no splitting will be performed.
--no-watermark: [DEPRECATED] Use –watermark-output-mode=no_watermark instead.
--translate-table-text: Translate table text (experimental, default: False)
--skip-scanned-detection: Skip scanned document detection (default: False). When using split translation, only the first part performs detection if not skipped.

[!TIP]

Both --skip-clean and --dual-translate-first may help improve compatibility with some PDF readers

--disable-rich-text-translate can also help with compatibility by simplifying translation input

However, using --skip-clean will result in larger file sizes

If you encounter any compatibility issues, try using --enhance-compatibility first

Use --max-pages-per-part for large documents to split them into smaller parts for translation and automatically merge them back.

Use --skip-scanned-detection to speed up processing when you know your document is not a scanned PDF.

Translation Service Options

--qps: QPS (Queries Per Second) limit for translation service (default: 4)
--ignore-cache: Ignore translation cache and force retranslation
--no-dual: Do not output bilingual PDF files
--no-mono: Do not output monolingual PDF files
--min-text-length: Minimum text length to translate (default: 5)
--openai: Use OpenAI for translation (default: False)

[!TIP]

Currently, only OpenAI-compatible LLM is supported. For more translator support, please use PDFMathTranslate.

It is recommended to use models with strong compatibility with OpenAI, such as: glm-4-flash, deepseek-chat, etc.

Currently, it has not been optimized for traditional translation engines like Bing/Google, it is recommended to use LLMs.

You can use litellm to access multiple models.

OpenAI Specific Options

--openai-model: OpenAI model to use (default: gpt-4o-mini)
--openai-base-url: Base URL for OpenAI API
--openai-api-key: API key for OpenAI service

[!TIP]

This tool supports any OpenAI-compatible API endpoints. Just set the correct base URL and API key. (e.g. https://xxx.custom.xxx/v1)

For local models like Ollama, you can use any value as the API key (e.g. --openai-api-key a).

Output Control

--output, -o: Output directory for translated files. If not set, use current working directory.
--debug, -d: Enable debug logging level and export detailed intermediate results in ~/.cache/yadt/working.
--report-interval: Progress report interval in seconds (default: 0.1).

Offline Assets Management

--generate-offline-assets: Generate an offline assets package in the specified directory. This creates a zip file containing all required models and fonts.
--restore-offline-assets: Restore an offline assets package from the specified file. This extracts models and fonts from a previously generated package.

[!TIP]

Offline assets packages are useful for environments without internet access or to speed up installation on multiple machines.

Generate a package once with babeldoc --generate-offline-assets /path/to/output/dir and then distribute it.

Restore the package on target machines with babeldoc --restore-offline-assets /path/to/offline_assets_*.zip.

The offline assets package name cannot be modified because the file list hash is encoded in the name.

If you provide a directory path to --restore-offline-assets, the tool will automatically look for the correct offline assets package file in that directory.

The package contains all necessary fonts and models required for document processing, ensuring consistent results across different environments.

The integrity of all assets is verified using SHA3-256 hashes during both packaging and restoration.

If you’re deploying in an air-gapped environment, make sure to generate the package on a machine with internet access first.

Configuration File

--config, -c: Configuration file path. Use the TOML format.

Example Configuration:

[babeldoc]
# Basic settings
debug = true
lang-in = "en-US"
lang-out = "zh-CN"
qps = 10
output = "/path/to/output/dir"

# PDF processing options
split-short-lines = false
short-line-split-factor = 0.8
skip-clean = false
dual-translate-first = false
disable-rich-text-translate = false
use-alternating-pages-dual = false
watermark-output-mode = "watermarked"  # Choices: "watermarked", "no_watermark", "both"
max-pages-per-part = 50  # Automatically split the document for translation and merge it back.
# no-watermark = false  # DEPRECATED: Use watermark-output-mode instead
skip-scanned-detection = false  # Skip scanned document detection for faster processing

# Translation service
openai = true
openai-model = "gpt-4o-mini"
openai-base-url = "https://api.openai.com/v1"
openai-api-key = "your-api-key-here"

# Output control
no-dual = false
no-mono = false
min-text-length = 5
report-interval = 0.5

# Offline assets management
# Uncomment one of these options as needed:
# generate-offline-assets = "/path/to/output/dir"
# restore-offline-assets = "/path/to/offline_assets_package.zip"

Python API

[!TIP]

Before pdf2zh 2.0 is released, you can temporarily use BabelDOC’s Python API. However, after pdf2zh 2.0 is released, please directly use pdf2zh’s Python API.

This project’s Python API does not guarantee any compatibility. However, the Python API from pdf2zh will guarantee a certain level of compatibility.

You can refer to the example in main.py to use BabelDOC’s Python API.

Please note:

Make sure call babeldoc.high_level.init() before using the API
The current TranslationConfig does not fully validate input parameters, so you need to ensure the validity of input parameters

For offline assets management, you can use the following functions:

# Generate an offline assets package
from pathlib import Path
import babeldoc.assets.assets

# Generate package to a specific directory
# path is optional, default is ~/.cache/babeldoc/assets/offline_assets_{hash}.zip
babeldoc.assets.assets.generate_offline_assets_package(Path("/path/to/output/dir"))

# Restore from a package file
# path is optional, default is ~/.cache/babeldoc/assets/offline_assets_{hash}.zip
babeldoc.assets.assets.restore_offline_assets_package(Path("/path/to/offline_assets_package.zip"))

# You can also restore from a directory containing the offline assets package
# The tool will automatically find the correct package file based on the hash
babeldoc.assets.assets.restore_offline_assets_package(Path("/path/to/directory"))

[!TIP]

The offline assets package name cannot be modified because the file list hash is encoded in the name.

When using in production environments, it’s recommended to pre-generate the assets package and include it with your application distribution.

The package verification ensures that all required assets are intact and match their expected checksums.

Background

There are a lot projects and teams working on to make document editing and translating easier like:

There are also some solutions to solve specific parts of the problem like:

layoutreader: the read order of the text block in a pdf
Surya: the structure of the pdf

This project hopes to promote a standard pipeline and interface to solve the problem.

In fact, there are two main stages of a PDF parser or translator:

Parsing: A stage of parsing means to get the structure of the pdf such as text blocks, images, tables, etc.
Rendering: A stage of rendering means to render the structure into a new pdf or other format.

For a service like mathpix, it will parse the pdf into a structure may be in a XML format, and then render them using a single column reader order as layoutreader does. The bad news is that the original structure lost.

Some people will use Adobe PDF Parser because it will generate a Word document and it keeps the original structure. But it is somewhat expensive. And you know, a pdf or word document is not a good format for reading in mobile devices.

We offer an intermediate representation of the results from parser and can be rendered into a new pdf or other format. The pipeline is also a plugin-based system which everybody can add their new model, ocr, renderer, etc.

Roadmap

Add line support
Add table support
Add cross-page/cross-column paragraph support
More advanced typesetting features
Outline support
…

Our first 1.0 version goal is to finish a translation from PDF Reference, Version 1.7 to the following language version:

Simplified Chinese
Traditional Chinese
Japanese
Spanish

And meet the following requirements:

layout error less than 1%
content loss less than 1%

Known Issues

Parsing errors in the author and reference sections; they get merged into one paragraph after translation.
Lines are not supported.
Does not support drop caps.
Large pages will be skipped.

How to Contribute

We encourage you to contribute to YADT! Please check out the CONTRIBUTING guide.

Everyone interacting in YADT and its sub-projects’ codebases, issue trackers, chat rooms, and mailing lists is expected to follow the YADT Code of Conduct.

Immersive Translation sponsors monthly Pro membership redemption codes for active contributors to this project, see details at: CONTRIBUTOR_REWARD.md