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I will do data science ai, machine and deep learning, ai and ml including ocr in python

I will do data science ai, machine and deep learning, ai and ml including ocr in python

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It seems like you’re interested in learning about data science, AI, machine learning, deep learning, and OCR in Python. There are many resources available online that can help you learn these topics. Here are some resources that might be helpful:

  • OCR with Keras, TensorFlow, and Deep Learning
  • Python Optical Character Recognition (OCR): A Tutorial
  • OCR: Handwriting recognition with OpenCV, Keras, and TensorFlow
  • Python OCR: Learn Optical Character Recognition from Scratch
  • Get started with deep learning OCR
  • Building Deep Learning-Based OCR Model: Lessons Learned

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.

Looking for the source code to this post?
OCR with Keras, TensorFlow, and Deep Learning
In the first part of this tutorial, we’ll discuss the steps required to implement and train a custom OCR model with Keras and TensorFlow.

We’ll then examine the handwriting datasets that we’ll use to train our model.

From there, we’ll implement a couple of helper/utility functions that will aid us in loading our handwriting datasets from disk and then preprocessing them.

Given these helper functions, we’ll be able to create our custom OCR training script with Keras and TensorFlow.

After training, we’ll review the results of our OCR work.

Let’s get started!

Our deep learning OCR datasets

Figure 1: We are using two datasets for our OCR training with Keras and TensorFlow. On the left, we have the standard MNIST 0-9 dataset. On the right, we have the Kaggle A-Z dataset from Sachin Patel, which is based on the NIST Special Database 19.
In order to train our custom Keras and TensorFlow model, we’ll be utilizing two datasets:

  • The standard MNIST 0-9 dataset by LeCun et al.
  • The Kaggle A-Z dataset by Sachin Patel, based on the NIST Special Database 19
The standard MNIST dataset is built into popular deep learning frameworks, including Keras, TensorFlow, PyTorch, etc. A sample of the MNIST 0-9 dataset can be seen in Figure 1 (left). The MNIST dataset will allow us to recognize the digits 0-9. Each of these digits is contained in a 28 x 28 grayscale image. You can read more about MNIST here.

But what about the letters A-Z? The standard MNIST dataset doesn’t include examples of the characters A-Z — how are we going to recognize them?

The answer is to use the NIST Special Database 19, which includes A-Z characters. This dataset actually covers 62 ASCII hexadecimal characters corresponding to the digits 0-9, capital letters A-Z, and lowercase letters a-z.

To make the dataset easier to use, Kaggle user Sachin Patel has released the dataset in an easy to use CSV file. This dataset takes the capital letters A-Z from NIST Special Database 19 and rescales them to be 28 x 28 grayscale pixels to be in the same format as our MNIST data.

For this project, we will be using just the Kaggle A-Z dataset, which will make our preprocessing a breeze. A sample of it can be seen in Figure 1 (right).

We’ll be implementing methods and utilities that will allow us to:
  1. Load both the datasets for MNIST 0-9 digits and Kaggle A-Z letters from disk
  2. Combine these datasets together into a single, unified character dataset
  3. Handle class label skew/imbalance from having a different number of samples per character
  4. Successfully train a Keras and TensorFlow model on the combined dataset
  5. Plot the results of the training and visualize the output of the validation data
Configuring your OCR development environment
To configure your system for this tutorial, I first recommend following either of these tutorials:
  • How to install TensorFlow 2.0 on Ubuntu
  • How to install TensorFlow 2.0 on macOS
Either tutorial will help you configure your system with all the necessary software for this blog post in a convenient Python virtual environment.

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