SRIF: Scale and Rotation Invariant Features for camera-based document image retrieval

In this paper, we propose a new feature vector, named Scale and Rotation Invariant Features (SRIF), for real-time camera-based document image retrieval. SRIF is based on Locally Likely Arrangement Hashing (LLAH), which has been widely used and accepted as an efficient real-time camera-based document image retrieval method based on text. SRIF is computed based on geometrical constraints between pairs of nearest points around a keypoint. It can deal with feature point extraction errors which are introduced as a result of the camera capturing of documents. The experimental results show that SRIF outperforms LLAH in terms of retrieval accuracy and processing time.