OCR with deep learning has revolutionized how we handle text from unstructured images. Based on your use case — simple documents, real-time scene text, or enterprise-level multilingual OCR...
In this first section, we will go over each approach to show how they differ. Later, we will list our top open-source OCR models and directly compare each one. Here’s a brief overview: Traditional OCR engines are purpose-built for text extraction.
In this article, you will learn about different lessons for building a deep learning-based OCR model so that when you are working on any such use case, you may not face the issues that I have faced during the development and deployment.
This article explores the powerful capabilities of OCR and presents a TensorFlow-based model, a testament to the evolution of deep learning in unlocking the potential of visual data.
This example shows how to perform text recognition by using a deep learning based text detector and OCR. In the example, you use a pretrained CRAFT (character region awareness for text) deep learning network to detect the text regions in the input image.
