python-doctr 0.2.0

Extract valuable text information from your documents

DocTR: Document Text Recognition

Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2

What you can expect from this repository:

• efficient ways to parse textual information (localize and identify each word) from your documents
• guidance on how to integrate this in your current architecture

Quick Tour

Getting your pretrained model

End-to-End OCR is achieved in DocTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations.

from doctr.models import ocr_predictor

model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)

Reading files

Documents can be interpreted from PDF or images:

from doctr.documents import DocumentFile
# PDF
pdf_doc = DocumentFile.from_pdf("path/to/your/doc.pdf").as_images()
# Image
single_img_doc = DocumentFile.from_images("path/to/your/img.jpg")
# Webpage
webpage_doc = DocumentFile.from_url("https://www.yoursite.com").as_images()
# Multiple page images
multi_img_doc = DocumentFile.from_images(["path/to/page1.jpg", "path/to/page2.jpg"])

Putting it together

Let's use the default pretrained model for an example:

from doctr.documents import DocumentFile
from doctr.models import ocr_predictor

model = ocr_predictor(pretrained=True)
# PDF
doc = DocumentFile.from_pdf("path/to/your/doc.pdf").as_images()
# Analyze
result = model(doc)

To make sense of your model's predictions, you can visualize them as follows:

result.show(doc)

or export them to JSON format (to get a better understanding of our document model, check our documentation):

json_output = result.export()

Installation

Python 3.6 (or higher) and pip are required to install DocTR.

You can install the latest release of the package using pypi as follows:

pip install python-doctr

Or you can install it from source:

git clone https://github.com/mindee/doctr.git
pip install -e doctr/.

Models architectures

Credits where it's due: this repository is implementing, among others, architectures from published research papers.

Text Detection

• Real-time Scene Text Detection with Differentiable Binarization.
• LinkNet: Exploiting Encoder Representations forEfficient Semantic Segmentation

Text Recognition

• An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition.
• Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition.

More goodies

Documentation

The full package documentation is available here for detailed specifications.

Demo app

A minimal demo app is provided for you to play with the text detection model!

You will need an extra dependency (Streamlit) for the app to run:

pip install -r demo/requirements.txt

You can then easily run your app in your default browser by running:

streamlit run demo/app.py

Docker container

If you are to deploy containerized environments, you can use the provided Dockerfile to build a docker image:

docker build . -t <YOUR_IMAGE_TAG>

Example script

An example script is provided for a simple documentation analysis of a PDF or image file:

python scripts/analyze.py path/to/your/doc.pdf

All script arguments can be checked using python scripts/analyze.py --help

Minimal API integration

Looking to integrate DocTR into your API? Here is a template to get you started with a fully working API.

Manual setup

Specific dependencies are required to run the API template, which you can install as follows:

pip install -r api/requirements.txt

You can now run your API locally:

uvicorn --reload --workers 1 --host 0.0.0.0 --port=8050 --app-dir api/ app.main:app

Docker setup

You can run the same server on a docker container if you prefer using:

PORT=8050 docker-compose up -d --build

Contributing

If you scrolled down to this section, you most likely appreciate open source. Do you feel like extending the range of our supported characters? Or perhaps submitting a paper implementation? Or contributing in any other way?

You're in luck, we compiled a short guide (cf. CONTRIBUTING) for you to easily do so!

License

Distributed under the Apache 2.0 License. See LICENSE for more information.