docTR: Document Text Recognition#
State-of-the-art Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
DocTR provides an easy and powerful way to extract valuable information from your documents:
🧾 for automation: seamlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents.
👩🔬 for research: quickly compare your own architectures speed & performances with state-of-art models on public datasets.
🤖 Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters
⚡ User-friendly, 3 lines of code to load a document and extract text with a predictor
🚀 State-of-the-art performance on public document datasets, comparable with GoogleVision/AWS Textract
⚡ Optimized for inference speed on both CPU & GPU
🐦 Light package, minimal dependencies
🛠️ Actively maintained by Mindee
🏭 Easy integration (available templates for browser demo & API deployment)
Text detection models#
Text recognition models#
SROIE from ICDAR 2019.
IIIT-5k from CVIT.
Street View Text from “End-to-End Scene Text Recognition”.
SynthText from Visual Geometry Group.
IC03 from ICDAR 2003.
IC13 from ICDAR 2013.
IIITHWS from “Generating Synthetic Data for Text Recognition”.