Abstract
In this paper, we describe an image based document retrieval system which runs on camera enabled mobile devices. “Mobile Retriever” aims to seamlessly link physical and digital documents by allowing users to snap a picture of the text of a document and retrieve its electronic version from a database. Experiments show that for a database of 100,093 pages, the correct document can be retrieved in less than 4 s at a success rate over 95%. Our system extracts token pairs from the text, to efficiently index and retrieve candidate pages using only a small portion of the image. We use token triplets that define the orientation of three corresponding tokens to effectively prune the false positives and identify the correct page to retrieve. We stress the importance of geometrical relationship between feature points and show its effectiveness in our camera based image retrieval system.
Similar content being viewed by others
References
Arai, T., Aust, D., Hudson, S.: PaperLink: a technique for hyperlinking from real paper to electronic content. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 327–334 (1997)
Brown P., Mercer R., Della Pietra V., Lai J.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992)
Doermann D.: The indexing and retrieval of document images: a survey. Comput. Vis. Image Understand. 70(3), 287–298 (1998)
Doermann D., Rivlin E., Rosenfeld A.: The function of documents. Int. J. Comput. Vis. 16, 799–814 (1998)
Doermann D., Rivlin E., Weiss I.: Applying algebraic and differential invariants for logo recognition. Mach. Vis. Appl. 9(2), 73–86 (1996)
Herrmann, P., Schlageter, G.: Retrieval of document images using layout knowledge. Document Analysis and Recognition. In: Proceedings of the Second International Conference on, pp. 537–540 (1993)
Kato, H., Tan, K.: Pervasive 2D barcodes for camera phone applications. IEEE Pervasive Computing, pp. 76–85 (2007)
Nakai, T., Kise, K., Iwamura, M.: Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval. Lecture Notes in Computer Science 7th International Workshop DAS2006, 3872:541–552 (2006)
Niblack, W.: An introduction to digital image processing (1990)
Smeaton, A., Spitz, A.: Using character shape coding for information retrieval. In: Proceedings of the 4th International Conference on Document Analysis and Recognition, p. 974 (1997)
Srihari, S., Shetty, S., Chen, S., Srinivasan, H., Huang, C., Agam, G., Frieder, O.: Document image retrieval using signatures as queries. In: Proceedings of the Second International Conference on Document Image Analysis for Libraries (DIAL’06), vol. 00, pp. 198–203 (2006)
Tan C., Huang W., Yu Z., Xu Y.: Imaged document text retrieval without OCR. Pattern Anal. Mach. Intell. IEEE Trans. 24(6), 838–844 (2002)
Wellner P.: Interacting with paper on the DigitalDesk. Commun. ACM 36(7), 87–96 (1993)
Liu, X., Doermann, D.: Mobile Retriever—Finding Document with a Snapshot. CBDAR 07, pp. 29–34, September 2007
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, X., Doermann, D. Mobile Retriever: access to digital documents from their physical source. IJDAR 11, 19–27 (2008). https://doi.org/10.1007/s10032-008-0066-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10032-008-0066-4