Building Deep Learning-Based OCR Model

In this blog post, we compare seven different OCR solutions and compare their efficacy in ten different areas of industrial OCR applications.

After reading the image into img (using cv2.imread()), reader.readtext runs OCR to detect and recognize all instances of text inside the image.

This is the second tutorial of the series explaining Optical Character Recognition OCR with deep learning.

This is an Optical Character Recognition Library with the ability to train and deploy Deep Neural Network models to a Streamlit Web application.

For example, image recognition is a common example of deep learning in action. ... example of the dataset for OCR training in a different ... TL;DR · The Basics and Challenges of... · Deep Learning and OCR...

Industry expert shares six lessons learned from constructing a deep learning-based OCR model, focusing on nuances and critical takeaways.

In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning.

Deep OCR is a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading.

Discover the secrets of Deep Learning-based OCR with Camilo Zapata, a seasoned Machine Learning Engineering manager at Landing AI.

I mean, deep learning OCR works on color images, can read almost any text in any condition (including handwriting), and can be online within ...