NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval
This work addresses the need for more transparent machine learning tools in historical palaeography, though it appears incremental as it adapts existing attention methods to a specific domain.
The authors tackled the problem of analyzing ancient Greek papyri images by developing NeuroPapyri, a deep learning model with an attention mechanism for improved interpretability, which demonstrated efficacy in document retrieval tasks.
The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical palaeography is often met with criticism due to their perceived ``black box'' nature. In response to this challenge, we introduce NeuroPapyri, an innovative deep learning-based model specifically designed for the analysis of images containing ancient Greek papyri. To address concerns related to transparency and interpretability, the model incorporates an attention mechanism. This attention mechanism not only enhances the model's performance but also provides a visual representation of the image regions that significantly contribute to the decision-making process. Specifically calibrated for processing images of papyrus documents with lines of handwritten text, the model utilizes individual attention maps to inform the presence or absence of specific characters in the input image. This paper presents the NeuroPapyri model, including its architecture and training methodology. Results from the evaluation demonstrate NeuroPapyri's efficacy in document retrieval, showcasing its potential to advance the analysis of historical manuscripts.