Content-based similar document image retrieval using fusion of CNN features

TL;DR

This paper introduces a content-based document image retrieval method that leverages multiple CNN features and their fusion to improve accuracy, especially for multilingual academic papers.

Contribution

It proposes a novel CNN feature fusion technique for content-based document image retrieval, enhancing accuracy without relying on OCR.

Findings

Fusion of CNN features improves retrieval accuracy.

Method effectively handles multilingual document images.

Experimental results show high effectiveness on academic papers.

Abstract

Rapid increase of digitized document give birth to high demand of document image retrieval. While conventional document image retrieval approaches depend on complex OCR-based text recognition and text similarity detection, this paper proposes a new content-based approach, in which more attention is paid to features extraction and fusion. In the proposed approach, multiple features of document images are extracted by different CNN models. After that, the extracted CNN features are reduced and fused into weighted average feature. Finally, the document images are ranked based on feature similarity to a provided query image. Experimental procedure is performed on a group of document images that transformed from academic papers, which contain both English and Chinese document, the results show that the proposed approach has good ability to retrieve document images with similar text content, and the fusion of CNN features can effectively improve the retrieval accuracy.