Signature and logo detection using deep CNN for document image retrieval

Document Type

Conference Article

Publication Title

Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR


Signature and logo as a query are important for content-based document image retrieval from a scanned document repository. This paper deals with signature and logo detection from a repository of scanned documents, which can be used for document retrieval using signature or logo information. A large intra-category variance among signature and logo samples poses challenges to traditional hand-crafted feature extraction-based approaches. Hence, the potential of deep learning-based object detectors namely, Faster R-CNN and YOLOv2 were examined for automatic detection of signatures and logos from scanned administrative documents. Four different network models namely ZF, VGG16, VGG-M, and YOLOv2 were considered for analysis and identifying their potential in document image retrieval. The experiments were conducted on the publicly available 'Tobacco-800' dataset. The proposed approach detects Signatures and Logos simultaneously. The results obtained from the experiments are promising and at par with the existing methods.

First Page


Last Page




Publication Date


This document is currently not available here.