Neocognitron - Recognition of Handwritten Characters

Neocognitron: Recognition of Handwritten Characters using Artificial Neural Networks

Definition

The Neocognitron is a hierarchical, multilayered artificial neural network designed for pattern recognition, particularly in the recognition of handwritten characters. It was introduced by Kunihiko Fukushima in 1980 and is considered an early model of convolutional neural networks (CNNs).

Key Concepts

• Hierarchical Structure: The Neocognitron consists of multiple layers of simple cells and complex cells, mimicking the visual cortex of the brain.
• Simple Cells (S-Cells): Responsible for detecting local features in input patterns.
• Complex Cells (C-Cells): Aggregate the outputs of simple cells, providing shift-invariant responses.
• Self-Organization: The network can learn to recognize patterns through unsupervised learning during the initial training phase.
• Feature Extraction and Pooling: Layers of the network alternately perform feature extraction and pooling, similar to modern CNNs.

Detailed Explanation

Recognition of handwritten characters using the Neocognitron involves the following steps:

1. Data Collection and Preprocessing:

   • Collect a dataset of images containing handwritten characters.
   • Preprocess the images by normalizing pixel values and ensuring uniform size and orientation.

2. Network Architecture:

   • Input Layer: Receives the raw input image.
   • S-Cell Layers: Detect local features such as edges, lines, and curves.
   • C-Cell Layers: Aggregate and pool the features detected by S-Cells, providing shift-invariant feature maps.
   • Higher Layers: Repeat the process of feature extraction and pooling, progressively capturing more complex patterns.

3. Training the Neocognitron:

   • Initial Training: Unsupervised learning phase where the network self-organizes to recognize basic features.
   • Supervised Training: Fine-tuning phase where labeled data is used to adjust the network parameters for specific character recognition tasks.
   • Use learning algorithms such as Hebbian learning for the unsupervised phase and gradient descent for the supervised phase.

4. Validation and Testing:

   • Validate the model using a validation set to fine-tune hyperparameters and assess the model's performance.
   • Test the final model on a separate test set to evaluate its accuracy in recognizing new handwritten characters.

5. Character Recognition:

   • Use the trained Neocognitron to classify new images of handwritten characters by feeding them into the network and observing the output.
   • The character corresponding to the highest activation in the output layer is selected as the recognized character.

Diagrams

Links to Resources

• Introduction to Neocognitron
• Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
• Handwritten Character Recognition using Neocognitron

Notes and Annotations

• Summary of key points:

  • The Neocognitron is a hierarchical, multilayered network designed for pattern recognition, particularly handwritten character recognition.
  • Key concepts include hierarchical structure, S-Cells, C-Cells, and self-organization.
  • The network's ability to learn features in an unsupervised manner and fine-tune with supervised learning is crucial for effective recognition.

• Personal annotations and insights:

  • The Neocognitron's structure inspired the development of modern CNNs, highlighting the importance of hierarchical feature extraction and pooling.
  • Incorporating unsupervised pre-training can significantly enhance the performance of networks in recognizing complex patterns such as handwritten characters.

Backlinks

• Artificial Neural Networks:
  • Introduction to ANN
  • Learning Algorithms
  • Convolution Neural Network
  • Applications of ANN