xei7 / mlp-character-recognition

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

MLP character recognition

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 8-bit array, which can then 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 with

var ocr = require('./ocr.js');
var output = ocr
  .activate(input)
  .map(function(bit) {
    return bit > 0.5 ? 1 : 0;
  });
  
// output is now binary array, converting to character is easy
var character = String.fromCharCode(parseInt(output.join(''), 2));

// do stuff ...

Performance

MNIST [0-9]

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

• Neurons
  • 400 input
  • 160 hidden
  • 4 output
• Learning rate: 0.03
• Training set: 60000 digits
• Testing set: 10000 digits
• Training time: 36 min 22 s 50 ms
• Success rate: 90.14%

[A-Za-z0-9]

// config.json
{
  "mnist": false,
  "text": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ012356789",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 1000,
  "image_size": 16,
  "threshold": 400,
  "network": {
    "hidden": 40,
    "learning_rate": 0.03
  }
}

• Neurons
  • 256 input
  • 40 hidden
  • 8 output
• Learning rate: 0.03
• Training set
  • Size: 124000 characters
  • Sample:
• Testing set: 62000 characters
• Training time: 4 min 8 s 2 ms
• Success rate: 78.60322580645162%

[a-z]

// 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
  }
}

• Neurons
  • 256 input
  • 40 hidden
  • 8 output
• Learning rate: 0.1
• Training set
  • Size: 52000 characters
  • Sample:
• Testing set: 26000 characters
• Training time: 2 min 10 s 752 ms
• Success rate: 91.77692307692308%

[0-9]

// 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
  • 8 output
• Learning rate: 0.1
• Training set
  • Size: 20000 digits
  • Sample:
• Testing set: 10000 digits
• Training time: 1 min 6 s 620 ms
• Success rate: 99.22%

Configuration

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": "abcdefghijklmnopqrstuvwxyz",
  "fonts": [
    "sans-serif",
    "serif"
  ],
  "training_set": 2000,
  "testing_set": 500,
  "image_size": 20,
  "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.

Usage

Clone this repository. The script is using canvas, so you'll need to install the Cairo rendering engine. On OSX, this can be done with the following one-liner (copied from canvas README):

$ wget https://raw.githubusercontent.com/LearnBoost/node-canvas/master/install -O - | sh

Then install npm dependencies and test it:

$ npm install
$ node main.js