This paper tackles the problem of evaluating the task of finding similar scanned historical watermarks - small images embedded in historical paper that have been digitized to be processed on a computer - using pre-trained neural networks. This research aims to identify an efficient and accurate alternative to the traditional, time-consuming manual detection methods for finding similar watermarks. The primary issue addressed is the inefficiency of these manual methods. The evaluation focuses on finding similar watermarks for a specific query watermark, assessing the efficacy of neural networks in comparison to a prior art system that employs traditional image processing techniques. This comparison aims to determine how well these neural networks perform in the task of watermark similarity detection. The study involves a dataset of 500 labeled images tested in two distinct contexts: one using unprocessed images and another using images processed to keep only the watermark outline. The results show that pre-trained models achieve higher accuracy and time efficiency compared to the prior art system that uses image processing. These models demonstrate significant effectiveness in watermark recognition and comparison, with each network achieving over 80% accuracy for traced watermarks. EfficientNetB0 achieved 94.66%, VGG16 89.33%, ResNet50 86.67%, and InceptionV3 84%, while the prior art system gets 64,8%. These results conclude that these models are valuable tools in the field of watermark recognition and comparison.

Watermarks are historical motifs present in the texture of paper that are commonly used to identify the paper manufacturers. They only become visible when viewed under certain light conditions. Under ideal circumstances, researchers may use watermarks to determine a historical document’s origins and context. To identify a watermark, it is matched to a previously archived watermark. Currently, this matching must be done manually, which is neither scalable nor parallelizable. Existing studies explore digital reconstructions of watermarks, but do not focus on a comparison-based setup. This report discusses a system that can automatically identify similar watermarks using traditional image processing techniques. The resulting system speeds up the process considerably, can be used on small datasets, and is more accessible to end-users. The system uses harmonization, feature extraction, and similarity matching. Harmonization involves improving the clarity of the watermark, which is often obscured by the material properties of the paper. Feature extraction involves finding useful information from the isolated watermarks, and similarity matching uses this information to score the similarity of a pair. We evaluated our system based on a dataset provided by the German Museum of Books and Writing. Over a broader range of quality, accuracy was found to be within the range of 41-53%. It was also found that improving watermark quality within the dataset improved accuracy results to around 82%. The system shows promise particularly with higher quality datasets. This report therefore demonstrates that traditional image processing techniques can be valuable when applied to situations where artificial intelligence may not be possible or efficient. Further research into this domain would be required to understand the advantages and limitations of image processing in comparison with artificial intelligence.