Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 (PyTorch in beta)
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.io 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.io 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 interactively as follows:
result.show(doc)
Or even rebuild the original document from its predictions:
import matplotlib.pyplot as plt
plt.imshow(result.synthesize()); plt.axis('off'); plt.show()
The ocr_predictor returns a Document object with a nested structure (with Page, Block, Line, Word, Artefact). To get a better understanding of our document model, check our documentation:
You can also export them as a nested dict, more appropriate for JSON format:
json_output = result.export()
For examples & further details about the export format, please refer to this section of the documentation
Installation
Prerequisites
Python 3.6 (or higher) and pip are required to install DocTR. Additionally, you will need to install at least one of TensorFlow or PyTorch.
Since we use weasyprint, you will need extra dependencies if you are not running Linux.
For MacOS users, you can install them as follows:
brew install cairo pango gdk-pixbuf libffi
For Windows users, those dependencies are included in GTK. You can find the latest installer over here.
Latest release
You can then install the latest release of the package using pypi as follows:
pip install python-doctr
We try to keep framework-specific dependencies to a minimum. But if you encounter missing ones, you can install framework-specific builds as follows:
# for TensorFlow
pip install python-doctr[tf]
# for PyTorch
pip install python-doctr[torch]
Developer mode
Alternatively, you can install it from source, which will require you to install Git. First clone the project repository:
git clone https://github.com/mindee/doctr.git
pip install -e doctr/.
Again, if you prefer to avoid the risk of missing dependencies, you can install the TensorFlow or the PyTorch build:
# for TensorFlow
pip install -e doctr/.[tf]
# for PyTorch
pip install -e doctr/.[torch]
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 for Efficient 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.
- MASTER: Multi-Aspect Non-local Network for Scene 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:
<div class="highlight highlight-source-shell position-relative" data-snippet-clipboard-copy-content="docker build . -t
“>
docker build . -t <YOUR_IMAGE_TAG>
