An Application-Independent and Segmentation-Free Approach for Spotting Queries in Document Images

We report our ongoing research on an application-independent and segmentation-free approach for spotting queries in document images. Built on our earlier work reported in [1][2], this paper introduces an image processing approach that finds occurrences of a query, which is a multi-part object, in a document image, through 5 steps: (1) Preprocessing for image normalization and connected components extraction. (2) Feature Extraction from connected components. (3) Matching of the query and document image connected components' feature vectors. (4) Voting for determining candidate occurrences in the document image that are similar to the query. (5) Candidate Filtering for detecting relevant occurrences and filtering out irrelevant patterns. Compared to existing methods, our contributions are twofold: Our approach is designed to deal with any type of queries, without restriction to a particular class such as words or mathematical expressions. Second, it does not apply a domain-specific segmentation to extract regions of interest from the document image, such as text paragraphs or mathematical calculations. Instead, it considers all the image information. Experimental evaluation using scanned journal images show promising performances and possibility of further improvement.