ICDAR 2019 Tutorial on Deep Learning for OCR, Document Analysis, Text Recognition, and Language Modeling
Thomas Breuel, NVIDIA Research, USA
Deep Learning has emerged as the predominant approach to many recognition tasks related to OCR and document analysis. The tutorial will cover applications of deep learning to problems in document analysis:
- models for OCR and text recognition, including recent developments
- DL approaches to layout analysis and preprocessing
- recent advances in DL models for language modeling and OCR
- obtaining training data; semi-supervised and unsupervised methods
- tools for large scale processing
The course will present numerous examples and workbooks based on PyTorch. Basic familiarity with deep learning and Python is recommended.
Reading List and Materials
Potential Target Audience
Graduate students and researchers interested in applying deep learning to OCR, scene text recognition, document analysis, and related areas.
There will be some overlap with last year’s DAS 2018 tutorial, and the tutorial will provide a self-contained introduction, but the focus will be on different topics, including the latest version of PyTorch, large scale processing, distillation, semi-supervised training, and distributed training. The tutorial is intended to be useful both to audience members who have seen last year’s tutorial and for audience members who are new to DL for document analysis.
Thomas Breuel works on deep learning and its applications at NVIDIA Research. Before that, he was a researcher at Google Brain, IBM, and Xerox PARC. He was a professor of computer science and head of the Image Understanding and Pattern Recognition (IUPR) at the University of Kaiserslautern. He has published numerous papers in document analysis, computer vision, and machine learning and is a contributor to several open source projects in OCR, document analysis, and machine learning.
This repository contains a number of worksheets and presentations that show how to use the collection of PyTorch-based OCRopus components.
The presentations are numbered, so it's recommended that you read the in order.
The companion worksheets containing full implementations of the models can be found on github