README.md
Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch
Project responsibility
docTR was originally created by Mindee. It is now actively developed and maintained by t2k GmbH.
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")
# Image
single_img_doc = DocumentFile.from_images("path/to/your/img.jpg")
# Webpage (requires `weasyprint` to be installed)
webpage_doc = DocumentFile.from_url("https://www.yoursite.com")
# 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")
# Analyze
result = model(doc)
Detecting the document layout
You can additionally run a layout detection model as part of