A Fast Appearance-Based Full-Text Search Method for Historical Newspaper Images

This paper presents a fast appearance-based full-text search method for historical newspaper images. Since historical newspapers differ from recent newspapers in image quality, type fonts and language usages, optical character recognition (OCR) does not provide sufficient quality. Instead of OCR approach, we adopted appearance-based approach; that means we matched character to character with its shapes. Assuming proper character segmentation and proper feature description, full-text search problem is reduced to sequence matching problem or feature vector. To increase computational efficiency, we adopted pseudo-code expression called LSPC, which is a compact sketch of feature vector while retaining a good deal of its information. Experimental result showed that our method can retrieve a query string from a text of over eight million characters within a second. In addition, we predict that more sophisticated algorithm could be designed for LSPC. As an example, we established the Extended Boyer-Moore-Hors pool algorithm that can reduce the computational cost further especially when the query string becomes longer.