In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning. **This post is the first in a two-part series on OCR with Keras and TensorFlow:**

**Part 1:***Training an OCR model with Keras and TensorFlow*(today’s post)**Part 2:***Basic handwriting recognition with Keras and TensorFlow*(next week’s post)

For now, we’ll primarily be focusing on how to train a custom Keras/TensorFlow model to recognize alphanumeric characters (i.e., the digits *0-9* and the letters *A-Z*).

Building on today’s post, next week we’ll learn how we can use this model to correctly classify handwritten characters in custom input images.

The goal of this two-part series is to obtain a deeper understanding of how deep learning is applied to the classification of handwriting, and more specifically, our goal is to:

- Become familiar with some well-known, readily available handwriting datasets for both digits and letters
- Understand how to train deep learning model to recognize handwritten digits and letters
- Gain experience in applying our custom-trained model to some real-world sample data
- Understand some of the challenges with real-world noisy data and how we might want to augment our handwriting datasets to improve our model and results

We’ll be starting with the fundamentals of using well-known handwriting datasets and training a ResNet deep learning model on these data.

**To learn how to train an OCR model with Keras, TensorFlow, and deep learning, just keep reading.**

#tensorflow #keras #deep learning

74.40 GEEK