Building an optical character recognizer using neural networks

This section describes the neural network based optical character identification scheme.

How to do it...

1. Import the following packages:

import numpy as np 
import neurolab as nl 

2. Read the input file:

in_file = 'words.data'

3. Consider 20 data points to build the neural network based system:

# Number of datapoints to load from the input file 
num_of_datapoints = 20

4. Represent the distinct characters:

original_labels = 'omandig' 
# Number of distinct characters 
num_of_charect = len(original_labels) 

5. Use 90% of data for training the neural network and the remaining 10% for testing:

train_param = int(0.9 * num_of_datapoints) 
test_param = num_of_datapoints - train_param 

6. Define the dataset extraction parameters:

s_index = 6 
e_index = -1 

7. Build the dataset:

information = [] 
labels = [] 
with open(in_file, 'r') as f: 
  for line in f.readlines(): 
    # Split the line tabwise 
    list_of_values = line.split('t') 

8. Implement an error check to confirm...