# Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents

arXiv: https://arxiv.org/abs/1907.09404 · HTML: https://mlsanity.com/paper/1907.09404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation](https://mlsanity.com/paper/2108.09433/) (2021)
- [Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing](https://mlsanity.com/paper/2208.02397/) (2022)

## Abstract (arXiv)

This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different sizes which are evaluated in terms of retrieval performance. A robust experimental protocol using two public datasets (Tobacoo-800 and DocExplore) has shown that the proposed methods compare favorably against state-of-the-art document image retrieval and pattern spotting methods.