Image Retrieval and Pattern Spotting on Historical Documents with Binary Descriptors

This paper describes a method to perform the tasks of Image Retrieval and Pattern Spotting in a collection of historical documents, in a zero-shot manner, i.e. without prior knowledge or any training on the pattern to be searched. The proposed method measures the similarity between images using representation schemes based on feature maps provided by intermediate layers of a CNN. Moreover, to improve the time response and reduce the storage need, we propose to binarize these image representations. Experimental results obtained on the DocExplore dataset show that the proposed method improves the mAP on this dataset by 38.46% for Image Retrieval and 134.6% for Pattern Spotting compared to the state-of-the-art methods. The proposed binarization strategy provides a reduction of memory usage by a factor of 16 with a slight decrease of less than one percentage point in the mAP for both tasks and a reduction of 13.3% in search time when using the binary representation.