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Luís A. Nunes Amaral
co-Director, Northwestern Institute on Complex Systems
Professor of Chemical & Biological Engineering
Professor of Physics & Astronomy (by courtesy)
Professor of Medicine (by courtesy)
Chemical & Biological Engineering
2145 Sheridan Road (Room E136)
Evanston, IL 60208, US
Phone:
(847) 491-7850![](/media/article_images/10032_2024_460_Fig3_HTML.detail.jpg)
On the improvement of handwritten text line recognition with octave convolutional recurrent neural networks
International Journal on Document Analysis and Recognition, (2024)
Abstract
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.