Implementing handwritten text recognition using deep learning with TensorFlow: An MNIST dataset approach

Handwritten text recognition (HTR) is a pivotal technology with extensive applications in document digitization, postal automation, and educational tools. This paper delves into the implementing a deep learning-based system for recognizing handwritten digits using TensorFlow and the MNIST dataset. The MNIST dataset, a widely-used benchmark, comprises 60,000 training images and 10,000 testing images of handwritten digits, each standardized to a 28x28 pixel grayscale format. Leveraging the power of Convolutional Neural Networks (CNNs), our model effectively extracts features and classifies digits with high accuracy. The model architecture consists of multiple convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. Preprocessing steps include normalizing pixel values and one-hot encoding the labels, ensuring the data is optimally formatted for training. The TensorFlow framework, known for its robustness and scalability, facilitates the development and deployment of the model. Through a series of experiments, the model demonstrates impressive performance, achieving high accuracy on the MNIST test set. This paper underscores the potential of deep learning in handwritten text recognition. It sets the stage for future enhancements, such as recognizing more complex handwritten texts and optimizing the system for practical applications. The results highlight the effectiveness of deep learning techniques in overcoming the challenges associated with handwritten text recognition, paving the way for advanced, real-world implementations.