Large scalability in document image matching using text retrieval
► We cast image matching as text retrieval. ► We have indexed more than 500,000 images. ► We provide recognition in approximately 500ms. We present a method that addresses image matching from partial blurry images by casting it as a problem of text retrieval. This allows us to leverage existing text...
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| Published in | Pattern recognition letters Vol. 33; no. 7; pp. 863 - 871 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-8655 1872-7344 |
| DOI | 10.1016/j.patrec.2011.10.013 |
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| Abstract | ► We cast image matching as text retrieval. ► We have indexed more than 500,000 images. ► We provide recognition in approximately 500ms.
We present a method that addresses image matching from partial blurry images by casting it as a problem of text retrieval. This allows us to leverage existing text document retrieval techniques and achieve efficiency and scalability similar to text search applications.
As an initial application, we present a document image matching system in which the user supplies a query image of a small patch of a paper document taken with a cell phone camera, and the system returns a label identifying the original electronic document if found in a previously indexed collection. We have implemented our method in a client server architecture. Feature computation on a mobile client is done in under 100ms, while end-to-end document recognition on a collection of more than 4300 pages requires approximately 500ms per image. Approximately 170ms is connection time and thus subject to network speed variations. We conclude presenting scalability results on a collection of nearly 500,000 documents. |
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| AbstractList | ► We cast image matching as text retrieval. ► We have indexed more than 500,000 images. ► We provide recognition in approximately 500ms.
We present a method that addresses image matching from partial blurry images by casting it as a problem of text retrieval. This allows us to leverage existing text document retrieval techniques and achieve efficiency and scalability similar to text search applications.
As an initial application, we present a document image matching system in which the user supplies a query image of a small patch of a paper document taken with a cell phone camera, and the system returns a label identifying the original electronic document if found in a previously indexed collection. We have implemented our method in a client server architecture. Feature computation on a mobile client is done in under 100ms, while end-to-end document recognition on a collection of more than 4300 pages requires approximately 500ms per image. Approximately 170ms is connection time and thus subject to network speed variations. We conclude presenting scalability results on a collection of nearly 500,000 documents. |
| Author | Moraleda, Jorge |
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| Snippet | ► We cast image matching as text retrieval. ► We have indexed more than 500,000 images. ► We provide recognition in approximately 500ms.
We present a method... |
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| Title | Large scalability in document image matching using text retrieval |
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