In this paper, we extend our earlier proposed feature descriptor named Scale and Rotation Invariant Features (SRIF) and a camera-based heterogeneous-content information spotting system based on the latter. Through its capacity to manage heterogeneous content in document images, SRIF represents an extension to existing strategies such as LLAH, which are dedicated to textual document images. This paper proposes new extensions of SRIF based on geometrical constraints between pairs of nearest points around a keypoint. SRIF has built-in capabilities to deal with feature point extraction errors which are introduced in camera-captured documents. To validate our method and compare it to the state-of-the-art, we have constructed three datasets of heterogeneous-content document images, along with the corresponding ground truths. Our experiment results confirm that SRIF outperforms the state-of-the-art in terms of processing time with equal or greater recall and precision for retrieval and spotting results.