Learn Handwritten Digits Recognition with Deep Learning in Tensorflow
Table of Contents
- Introduction
- Basics of Handwritten Digits Classification
- Image Representation
- Data Preprocessing
- Deep Learning Architecture for Handwritten Digits Classification
- Convolutional Layers
- Activation Function
- Pooling Layers
- Flattening Layer
- Dense Layers
- Output Layer and Softmax Activation
- Training and Evaluation
- Splitting Data into Training and testing Sets
- Compiling the Model
- Training the Model
- Evaluating the Model
- Test on Custom Handwritten Digits
- Data Preprocessing
- Classification Results
- Conclusion
- FAQ
Introduction
In this Tutorial, we will be implementing handwritten digits classification using TensorFlow. This tutorial is targeted towards extreme beginners who are starting their career in the field of computer vision and deep learning. We will show you a demo of how a digit can be recognized by a deep learning architecture.
Basics of Handwritten Digits Classification
Image Representation
In computer vision, an image is represented as a set of integer values. Each pixel in the image corresponds to an integer value. For example, if the pixel value is 255, the pixel is white in color, and if the pixel value is 0, the pixel is black in color. The values in between represent shades of gray.
Data Preprocessing
Before extracting features for image classification, it is important to preprocess the data. This involves normalizing the pixel values and dividing the dataset into training and testing sets. The MNIST dataset, which contains 60,000 handwritten digits, is commonly used for training and generalizing the solution.
Deep Learning Architecture for Handwritten Digits Classification
Convolutional Layers
Convolutional layers are used for feature extraction in deep learning architectures. They consist of multiple filters or kernels that convolve with the input image to extract features based on their weights. The resulting feature maps are then passed through activation functions to introduce non-linearity into the model.
Activation Function
Activation functions are applied to the outputs of convolutional layers to introduce non-linearity. Commonly used activation functions include ReLU (Rectified Linear Unit), which replaces negative values with zero, and softmax, which is used in the output layer for multi-class classification.
Pooling Layers
Pooling layers are used to reduce the size of feature maps while retaining the most important features. Max pooling and average pooling are two common pooling techniques. Max pooling selects the maximum value in each pooling window, while average pooling calculates the average value.
Flattening Layer
The flattening layer converts the two-dimensional feature maps into a one-dimensional vector. It is necessary to flatten the feature maps before passing them to the fully connected layers.
Dense Layers
Dense layers, also known as fully connected layers, are typical neural network layers. Each neuron in a dense layer is connected to all neurons in the previous layer. Dense layers aggregate and process the features extracted by the convolutional layers.
Output Layer and Softmax Activation
The output layer of the deep learning architecture must have the same number of neurons as the number of classes. In the case of handwritten digits classification, there are 10 classes (0-9), so the output layer will have 10 neurons. The softmax activation function is usually applied to the output layer for multi-class classification, as it provides class probabilities.
Training and Evaluation
Splitting Data into Training and Testing Sets
To train and generalize our solution, it is necessary to have a dataset that contains both training and testing samples. In our case, we are using the MNIST dataset, which contains 60,000 images for training and 10,000 images for testing. The dataset consists of handwritten digits ranging from 0 to 9, written by different individuals in different styles.
Compiling the Model
Before training the model, it is important to compile it with appropriate loss and optimization functions. The loss function used in our case is sparse categorical cross-entropy, which is suitable for multi-class classification. The Adam optimizer is commonly used in deep learning architectures due to its efficiency.
Training the Model
The model is trained using the training dataset, and the accuracy is monitored throughout the training process. The validation accuracy is also evaluated to ensure that the model generalizes well. Overfitting, which occurs when the model performs well on the training data but poorly on the testing data, should be avoided.
Evaluating the Model
Once the model is trained, it is evaluated using the testing dataset to measure its performance on unseen data. The test accuracy is calculated to determine how well the model classifies handwritten digits.
Test on Custom Handwritten Digits
Data Preprocessing
To test the model on custom handwritten digits, the image needs to undergo the same preprocessing steps as the training data. This includes converting the image to grayscale, resizing it to the same Dimensions as the training images, normalizing the pixel values, and reshaping it to match the input size of the model.
Classification Results
The model can then predict the class of the custom handwritten digit. The predicted class corresponds to the digit in the image.
Conclusion
In this tutorial, we implemented a deep learning architecture for handwritten digits classification using TensorFlow. We discussed the basics of image representation, data preprocessing, and the different layers involved in the architecture. The model was trained and evaluated on the MNIST dataset, achieving high accuracy on both the training and testing sets. We also demonstrated how the model can be used to classify custom handwritten digits.
FAQ
Q: How accurate is the model's classification of handwritten digits?
A: The model achieves a high accuracy rate, with the test accuracy reaching approximately 98%.
Q: Can the model classify digits written in different styles or Handwriting?
A: Yes, the model is trained on a diverse dataset of handwritten digits, so it can classify digits written in different styles by different individuals.
Q: Is it necessary to normalize the pixel values in image preprocessing?
A: Yes, normalizing the pixel values to a range of 0 to 1 helps improve the model's performance and convergence during training.
Q: What other datasets can be used for training handwritten digit classification models?
A: Besides the MNIST dataset, other datasets such as the USPS dataset and the SVHN dataset can be used for training handwritten digit classification models.
Q: Can the model be extended to classify digits written in languages other than English?
A: Yes, the model can be trained on datasets containing digits written in languages other than English. However, a larger and more diverse dataset would be required to achieve accurate classification.
Q: Can the model be adapted for real-time digit classification using a webcam?
A: Yes, the model can be used for real-time digit classification by capturing video frames from a webcam and passing them through the model for prediction.
Q: How can the model's performance be further improved?
A: The model's performance can be improved by optimizing hyperparameters, increasing the depth of the architecture, adding more training data, or using more advanced techniques such as data augmentation and transfer learning.
Q: Is the trained model reusable for other image classification tasks?
A: The trained model can be reutilized as a base model for similar image classification tasks. Fine-tuning or transfer learning techniques can be used to adapt the model to new classification tasks.
