On the improvement of handwritten text line recognition with octave convolutional recurrent neural networks

Off-line handwritten text recognition (HTR) poses a significant challenge due to the complexities of variable handwriting styles, background degradation, and unconstrained word sequences. This work tackles the handwritten text line recognition problem using octave convolutional recurrent neural networks (OctCRNN). Our approach requires no word segmentation, preprocessing, or explicit feature extraction and leverages octave convolutions to process multiscale features without increasing the number of learnable parameters. We investigate the OctCRNN under different settings, including an octave design that efficiently balances computational cost and recognition performance. We thoroughly investigate the OctCRNN under different settings by formulating an experimental pipeline with a visualization step to get intuitions about how the model works compared to a counterpart based on traditional convolutions. The system becomes complete by adding a language model to increase linguistic knowledge. Finally, we assess the performance of our solution using character and word error rates against established handwritten text recognition benchmarks: IAM, RIMES, and ICFHR 2016 READ. According to the results, our proposal achieves state-of-the-art performance while reducing the computational requirements. Our findings suggest that the architecture provides a robust framework for building HTR systems.