In this paper, we propose camera-based document retrieval systems 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 image captured by cameras. Notably, 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 X 11768 pixels resolution). The experimental results show that SRIF outperforms the state-of-the-art in terms of retrieval time with 91.9% retrieval accuracy.