Recognition of Printed Characters

Recognition of Printed Characters using Artificial Neural Networks

Definition

Recognition of Printed Characters refers to the process of identifying and classifying characters (letters, numbers, symbols) from printed text using Artificial Neural Networks. This process is commonly known as Optical Character Recognition (OCR) and involves converting images of printed text into machine-encoded text.

Key Concepts

• Optical Character Recognition (OCR): The electronic conversion of images of typed, handwritten, or printed text into machine-encoded text.
• Image Preprocessing: Techniques used to prepare images of text for analysis, such as binarization, normalization, and noise reduction.
• Convolutional Neural Networks (CNNs): A class of deep neural networks particularly effective for image recognition tasks.
• Recurrent Neural Networks (RNNs): A class of neural networks suitable for sequential data, often used in conjunction with CNNs for OCR tasks.
• Character Segmentation: The process of dividing text images into individual characters.
• Softmax Function: An activation function used in the output layer of a classification network to produce probability distributions over classes.

Detailed Explanation

Recognition of Printed Characters using ANNs typically involves the following steps:

1. Data Collection and Preprocessing:

   • Collect a dataset of images containing printed characters.
   • Preprocess the images by converting them to grayscale, binarizing them to enhance contrast, normalizing pixel values, and applying noise reduction techniques to improve clarity.

2. Character Segmentation:

   • Segment the images into individual characters using methods such as connected component analysis or contour detection.
   • Ensure each segmented character is resized to a standard size suitable for the neural network.

3. Designing the CNN/RNN:

   • Choose an appropriate architecture, often a combination of CNNs for feature extraction and RNNs for sequence modeling.
   • Design the network with convolutional layers for extracting features from character images, followed by recurrent layers for capturing sequential dependencies, and fully connected layers for classification.

4. Training the Network:

   • Use a labeled dataset of segmented characters to train the network.
   • Employ a loss function such as categorical cross-entropy to measure classification error.
   • Optimize the network using an algorithm like stochastic gradient descent (SGD) or Adam.

5. Validation and Testing:

   • Validate the model using a validation set to fine-tune hyperparameters and prevent overfitting.
   • Test the final model on a separate test set to evaluate its performance and accuracy.

6. Character Recognition:

   • Use the trained network to classify new images of printed characters by feeding them into the network and observing the output probabilities.
   • The character corresponding to the highest probability is selected as the recognized character.

Diagrams

Links to Resources

• Optical Character Recognition (OCR) Overview
• Deep Learning for OCR
• OCR with CNNs and RNNs

Notes and Annotations

• Summary of key points:

  • OCR involves image preprocessing, character segmentation, network design, training, and testing.
  • Key concepts include OCR, CNNs, RNNs, character segmentation, and the softmax function.
  • Effective recognition requires careful preprocessing, segmentation, and selection of network architecture.

• Personal annotations and insights:

  • Combining CNNs and RNNs can significantly improve OCR performance by leveraging the strengths of both architectures in handling spatial and sequential data.
  • Data augmentation techniques such as adding synthetic noise and varying fonts can enhance model robustness by providing diverse training samples.

Backlinks

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