Feature Mixing For Writer Retrieval And Identification On Papyri Fragments

Abstract

This paper proposes a deep-learning-based approach to writer retrieval and identification for papyri, with a focus on identifying fragments associated with a specific writer and those corresponding to the same image. We present a novel neural network architecture that combines a residual backbone with a feature mixing stage to improve retrieval performance, and the final descriptor is derived from a projection layer. The methodology is evaluated on two benchmarks: PapyRow, where we achieve a mAP of 26.6 % and 24.9 % on writer and page retrieval, and HisFragIR20, showing state-of-the-art performance (44.0 % and 29.3 % mAP). Furthermore, our network has an accuracy of 28.7 % for writer identification. Additionally, we conduct experiments on the influence of two binarization techniques on fragments and show that binarizing does not enhance performance. Our code and models are available to the community.

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keypeer2023feature

Related papers

• Writer Retrieval And Writer Identification In Greek Papyri (2022)8.60
• Writer Identification And Writer Retrieval Based On Netvlad With Re-ranking (2020)8.82
• Re-ranking For Writer Identification And Writer Retrieval (2020)7.81
• Pairingnet: A Learning-based Pair-searching And -matching Network For Image Fragments (2023)4.52
• SAGHOG: Self-supervised Autoencoder For Generating HOG Features For Writer Retrieval (2024)4.52
• Self-supervised Vision Transformers For Writer Retrieval (2024)5.24
• Deep Learning Approaches For Image Retrieval And Pattern Spotting In Ancient Documents (2019)0.00
• Fused Text Recogniser And Deep Embeddings Improve Word Recognition And Retrieval (2020)4.52