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. This technology has wide-ranging applications in education, healthcare, and business, where digitizing handwritten content can streamline accessibility and automation.

We implemented a hybrid architecture combining a Convolutional Neural Network (CNN) for image feature extraction and Gated Recurrent Units (GRUs) for sequence modeling. GRUs were chosen over LSTMs due to their lower computational requirements and faster training times, making them more suitable given project constraints. To address the sequential nature of handwritten text, the model was trained using Connectionist Temporal Classification (CTC) loss, which enabled alignment-free recognition. Compared to traditional approaches such as Hidden Markov Models, this architecture captured broader contextual information, improving recognition accuracy for continuous sequences.

The IAM Handwriting Database served as the foundation for training. To ensure fairness, we created a balanced dataset accounting for word length variations and adopted a strict train–validation–test split protocol to preserve evaluation integrity.

As a baseline, we designed a ResNet152-based model that first segmented word images into individual characters and then performed classification. While effective for isolated symbols, this approach struggled with natural handwriting continuity. Our final CNN + GRU model achieved a test accuracy of 52%, significantly outperforming the baseline accuracy of 29%. This demonstrated the advantage of handling handwriting as a sequence prediction task rather than a character-level classification task.

The project not only met but exceeded initial expectations, showcasing practical readiness for deployment in real-world applications. Beyond academic evaluation, it highlighted the potential of deep learning architectures to push OCR technology toward broader adoption across industries.