Farsi Handwritten Text Recognition via a Lightweight Attention-Driven Sequence Recognition Network

In today's digital age, there's a growing demand for a dependable system that can recognize handwritten text. This demand comes from various industries as well as individual users, highlighting the importance of a system that can quickly convert, store, and enhance the accessibility of handwritten documents. Encoder-decoder models have proven their effectiveness in various sequence learning tasks, such as machine translation, image captioning, action recognition, and optical character recognition. However, these models have encountered difficulties in achieving competitive results in the specific context of Farsi handwritten text recognition. This paper introduces an innovative lightweight attention-driven encoder-decoder model designed to address the challenges associated with Farsi handwritten text recognition. To evaluate the proposed model, two well-known Farsi handwritten text datasets, Sadri and Iranshahr, are utilized. Additionally, to potentially enhance recognition accuracy, two different convolutional backbones-ResNet34 and MobileNetV3-are explored. Through experimental analysis, our best-performing model surpasses existing CNN-based and CRNN-based methods, achieving recognition accuracies of 94.79% on the Iranshahr dataset and 98.99% on the Sadri dataset. Additionally, our model offers faster inference speeds and a lower computational burden compared to recent Transformer-based approaches. These results demonstrate the effectiveness of our attention-driven encoder-decoder model in addressing the challenges of lightweight Farsi handwritten text recognition.