Trains a multi-layer perceptron (MLP) neural network to perform optical character recognition (OCR).

The training set is automatically generated using a heavily modified version of the captcha-generator node-captcha. Support for the MNIST handwritten digit database has been added recently (see performance section).

The network takes a one-dimensional binary array (default 20 * 20 = 400-bit) as input and outputs an 10-bit array of probabilities, which can be converted into a character code. Initial performance measurements show promising success rates.

After training, the network is saved as a standalone module to ./ocr.js, which can then be used in your project like this (from test.js):

var predict = require('./ocr.js');

// a binary array that we want to predict
var one = [
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
];

// the prediction is an array of probabilities
var prediction = predict(one);

// the index with the maximum probability is the best guess
console.log('prediction:', prediction.indexOf(Math.max.apply(null, prediction)));
// will hopefully output 1 if trained with 0-9 :)

All runs below were performed with a MacBook Pro Retina 13" Early 2015 with 8GB RAM.

To test with the MNIST dataset: click on the title above, download the 4 data files and put them in a folder called mnist in the root directory of this repository.

// config.json
{
  "mnist": true,
  "network": {
    "hidden": 160,
    "learning_rate": 0.03
  }
}

Then run

$ node mnist.js

• Neurons
  • 400 input
  • 160 hidden
  • 10 output
• Learning rate: 0.03
• Training set: 60000 digits
• Testing set: 10000 digits
• Training time: 21 min 53 s 753 ms
• Success rate: 95.16%

// config.json
{
  "mnist": false,
  "text": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ012356789",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 1000,
  "image_size": 16,
  "threshold": 400,
  "network": {
    "hidden": 60,
    "learning_rate": 0.1,
    "output": 62
  }
}

• Neurons
  • 256 input
  • 60 hidden
  • 62 output
• Learning rate: 0.03
• Training set
  • Size: 124000 characters
  • Sample:
• Testing set: 62000 characters
• Training time: 8 min 18 s 560 ms
• Success rate: 93.58225806451614%

// config.json
{
  "mnist": false,
  "text": "abcdefghijklmnopqrstuvwxyz",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 1000,
  "image_size": 16,
  "threshold": 400,
  "network": {
    "hidden": 40,
    "learning_rate": 0.1,
    "output": 26
  }
}

• Neurons
  • 256 input
  • 40 hidden
  • 26 output
• Learning rate: 0.1
• Training set
  • Size: 52000 characters
  • Sample:
• Testing set: 26000 characters
• Training time: 1 min 55 s 414 ms
• Success rate: 93.83846153846153%

// config.json
{
  "mnist": false,
  "text": "0123456789",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 1000,
  "image_size": 16,
  "threshold": 400,
  "network": {
    "hidden": 40,
    "learning_rate": 0.1
  }
}

• Neurons
  • 256 input
  • 40 hidden
  • 10 output
• Learning rate: 0.1
• Training set
  • Size: 20000 digits
  • Sample:
• Testing set: 10000 digits
• Training time: 0 min 44 s 363 ms
• Success rate: 99.59%

Tweak the network for your needs by editing the config.json file located in the main folder. Pasted below is the default config file.

// config.json
{
  "mnist": false,
  "text": "0123456789",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 1000,
  "image_size": 16,
  "threshold": 400,
  "network": {
    "hidden": 40,
    "learning_rate": 0.1
  }
}

• mnist
  • If set to true, the MNIST handwritten digit dataset will be used for training and testing the network. This setting will overwrite configured set sizes and will ignore the image_size, threshold, fonts and text settings.
• text
  • A string containing the glyphs with which to train/test the network.
• fonts
  • An array of fonts to be used when generating images.
• training_set
  • Number of images to be generated and used as the network training set.
• testing_set
  • Same as above, but these images are used for testing the network.
• image_size
  • The size of the square chunk (in pixels) containing a glyph. The resulting network input size is image_size^2.
• threshold
  • When analyzing the pixels of a glyph, the algorithm reduces each pixel (r, g, b) to (r + g + b) and everything below threshold is marked as 1 in the resulting binary array used as network input.
• network
  • hidden
    • The size (number of neurons) of the hidden layer of the network.
  • learning_rate
    • The learning rate of the network.