Layouts and sub-layouts constitute an important clue while searching a document on the basis of its structure, or when textual content is unknown irrelevant. A sub-layout specifies the arrangement of document entities within a smaller portion of the document. We propose an efficient graph-based matching algorithm, integrated with hash-based indexing, to prune a possibly large search space. A user can specify a combination of sub-layouts of interest using sketch-based queries. The system supports partial matching for unspecified layout entities. We handle cases of segmentation pre-processing errors (for text non-text blocks) with a symmetry maximization-based strategy, and accounting for multiple domain-specific plausible segmentation hypotheses. We show promising results of our system on a database of unstructured entities, containing 4776 newspaper images.
The above systems work on the basis of the layout of the complete document image, and not specific sub-layouts. Shin and Doermann @cite_6 describe a system for sub-layout based document matching. They measure the query-to-document similarity by comparing the edges of blocks at approximately the same location in the query and the candidate image, after scale normalization. Thus, a solution is required for the problem of sub-layout-based search where the query layout can be present at different scales and translations. Fig. shows an example of query image and the retrieved image from the database using our method, whose layout entities are of different size, aspect ratio and the layout itself is at a position different from the layout in the query image. For images in this paper, cyan blue represents text, pink red represent non-text non-background blocks, and grey indicates that the specific block type (text non-text) is irrelevant.
@cite_6
205301110
In this paper, we describe issues related to the measurement of structural similarity between document images. We define structural similarity, and discuss the benefits of using it as a complement to content similarity for querying document image databases. We present an approach to computing a geometrically invariant structural similarity, and use this measure to search document image databases. Our approach supports both full image matching using query by example (QBE) and sub-image matching using query by sketch (QBS). The similarity measure considers spatial and layout structure, and is computed by aggregating content area overlap measures with respect to their underlying column structures. These techniques are tested within the Intelligent Document Image Retrieval (IDIR) System, and results demonstrating effectiveness and efficiency of structure queries with respect to human relevance judgments are presented.