Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR

• URL: http://arxiv.org/abs/2401.12513v2
• Date: Wed, 14 Feb 2024 01:40:52 GMT
• Title: Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR
• Authors: Robert Turnbull and Evelyn Mannix
• Abstract summary: This paper discusses our submission to the ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri' We used an ensemble of YOLOv8 models to detect and classify individual characters and employed two different approaches for refining the character predictions. Our submission won the recognition challenge with a mAP of 42.2%, and was runner-up in the detection challenge with a mean average precision (mAP) of 51.4%.
• Score: 9.7902367664742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Purpose: The capacity to isolate and recognize individual characters from facsimile images of papyrus manuscripts yields rich opportunities for digital analysis. For this reason the `ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri' was held as part of the 17th International Conference on Document Analysis and Recognition. This paper discusses our submission to the competition. Methods: We used an ensemble of YOLOv8 models to detect and classify individual characters and employed two different approaches for refining the character predictions, including a transformer based DeiT approach and a ResNet-50 model trained on a large corpus of unlabelled data using SimCLR, a self-supervised learning method. Results: Our submission won the recognition challenge with a mAP of 42.2%, and was runner-up in the detection challenge with a mean average precision (mAP) of 51.4%. At the more relaxed intersection over union threshold of 0.5, we achieved the highest mean average precision and mean average recall results for both detection and classification. Conclusion: The results demonstrate the potential for these techniques for automated character recognition on historical manuscripts. We ran the prediction pipeline on more than 4,500 images from the Oxyrhynchus Papyri to illustrate the utility of our approach, and we release the results publicly in multiple formats.

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