This paper presents a method of planar object recognition for aiming at accessing information about objects by taking pictures of them. For this purpose efficiency of processing is the central issue because current state-of-the-art technologies with tree structures do not necessarily work well with a large amount of data represented as high dimensional vectors. To solve this problem, we employ hashing of keypoints extracted from images of objects. With the help of hash keys obtained as integers converted from the real valued vectors, keypoints are stored with object IDs and retrieved with no search process. Voting for object IDs is employed to determine a recognized object as the one with the largest vote. Experimental results show that the proposed method is at least 400 times faster than a brute-force method while 90% of objects were correctly recognized.