Efficient Cut-Off Threshold Estimation for Word Spotting Applications

Word spotting is an alternative methodology for document indexing based on spotting words directly on document images with the help of efficient word matching while avoiding conventional OCR procedure. The result of the word spotting procedure is a list of word images ranked according to a certain similarity criterion. In this paper, we propose an efficient method to cut-off the ranked list in order to provide the best tradeoff between recall and precision rates. Our aim is to filter the most relevant results based on a threshold which corresponds to an approximate maximization of the expected F-Measure. This is achieved by introducing an estimator that combines the distance of each ranked word with its cumulative moving average. Experimental results on a database with representative historical printed documents prove the efficiency of the proposed approach.