Does Super-Resolution Improve OCR Performance in the Real World ? A Case Study on Images of Receipts

Vivien Robert (1) , Hugues Talbot (2)

1 Artefact [Paris]
2 OPIS - OPtimisation Imagerie et Santé

Recently, many deep learning methods have been used to handle single image super-resolution (SISR) tasks and often achieve state-of-the-art performance. From a visual point of view, the results look convincing. Yet, does it mean that those techniques are reliable and robust enough to be implemented in real business cases to enhance the performance of other computer vision tasks? In this article, we investigate the use of SISR to construct higher-resolution images of real receipt photos sent by a company's customers and evaluate its impact on the performance of an OCR task (receipt information retrieval). Using built-in task-based performance evaluation methods, we show that the use of SISR can significantly improve OCR performance in the case where recognition was poor in low-resolution, but can also deteriorate the performance for receipts that were already successfully recognized. As a conclusion, we provide recommendations on how to best use SISR in a production environment.

Mots clés: Single image super-resolution; deep learning; task-based performance evaluation; image restoration

Domaines: Traitement des images [eess.IV]; Algorithme et structure de données [cs.DS]; Bio-informatique [q-bio.QM]; Optimisation et contrôle [math.OC]

Vivien Robert, Hugues Talbot. Does Super-Resolution Improve OCR Performance in the Real World ? A Case Study on Images of Receipts. ICIP 2020 - IEEE International Conference on Image Processing, Oct 2020, Abu Dhabi, United Arab Emirates. pp.548-552, ⟨10.1109/ICIP40778.2020.9191067⟩. ⟨hal-03144925⟩