Abstract
In this paper, we propose a camera-based document retrieval system using various local features as well as indexing methods. For feature extraction, we use well known features such as LLAH, SIFT, SURF and ORB that are invariant to image transformations and work well with images captured by cameras. In addition, we employ our new features, named as Scale and Rotation Invariant Features (SRIF). SRIF is computed based on geometrical constraints between pairs of nearest points around a keypoint. Our systems are applied on dataset including 400 heterogeneous-content complex linguistic map images (huge size, 9800\(\,\times \,\)11768 pixels resolution). The experimental results show that the system using SRIF is efficient in terms of retrieval time with 95.2% retrieval accuracy.
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Notes
- 1.
It can be downloaded from http://navidomass.univ-lr.fr/SRIFDataset/.
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Acknowledgment
This work has been partially supported by the LabEx PERSYVAL Lab (ANR-11-LABX-0025), by the CNRS PEPS Project CartoDialect, by the Program 165 of Vietnamese government by the ECLATS project funded by the French National Research Agency (ANR) under the grant ANR-15-CE-380002. The authors would like to thank Ms. MARWA MANSRI and Ms. TRAN HUYNH LE who helped us to construct the dataset and ground truth.
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Quoc Dang, B., Le Viet, P., Luqman, M.M., Coustaty, M., Tran Cao, D., Ogier, JM. (2017). A System for Camera-Based Retrieval of Heterogeneous-Content Complex Linguistic Map. In: Lamiroy, B., Dueire Lins, R. (eds) Graphic Recognition. Current Trends and Challenges. GREC 2015. Lecture Notes in Computer Science(), vol 9657. Springer, Cham. https://doi.org/10.1007/978-3-319-52159-6_7
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