Tags: PythonPyTorchMatplotlibPandasIAM Handwriting DatabaseCNNGRUDeep Learning

The project aimed to develop a deep-learning model capable of Optical Character Recognition (OCR) to transform handwritten text into digital format. If we think about it, this technology has potential applications in various fields including education, healthcare, and business.

Unlike traditional methods, such as Hidden Markov Models, Recurrent Neural Network (RNN) based architectures like Gated Recurrent Unit (GRU) and Long short-term memory (LSTM), can consider larger contextual information making it more effective for the sequential nature of handwritten text.

We utilized a combination of Convolutional Neural Network (CNN) for initial image processing and GRUs for sequence analysis, employing Connectionist Temporal Classification (CTC) loss for training the model.

The IAM Handwriting Database was used to create a balanced training dataset to prevent biases related to word length. We ensured a rigorous splitting protocol for training, validation, and testing to maintain integrity in model evaluation.

A ResNet152-based model served as our baseline to compare improvements. Our final model significantly outperformed the baseline (52% vs 29% test accuracy), demonstrating the effectiveness of our chosen architecture in handling real-world handwritten text.

The project not only met but exceeded initial expectations in terms of accuracy and application readiness, marking a significant step forward in the practical use of OCR technologies in diverse real-world scenarios.