Searching for the most similar matches to high dimensional feature vectors is the most computationally expensive part of many computer vision and document retrieval systems. This work proposes a time-efficient document retrieval system based on logo spotting. The spotting approach is based on feature matching and grouping. In order to reduce the number of key- point features to be matched, we propose to utilize a text/non-text separation method to get rid ABSTRACT.of text layer features which are irrelevant to logo matching. The separation method is used as a fast and effective preprocessing step. We further optimize the key-point feature matching step by using an approximate nearest neighbor search algorithms. The overall document retrieval with focused logo retrieval is evaluated on the standard Tobacco-800 database and also our private advertisement magazine database. The results show that the two proposed speed up steps – specially the text separation – reduce the computation time of the system sharply by 75% and 47% on the two databases respectively, while its precision remains unaffected.