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This paper presents a two-phased stroke-like pattern noise (SPN) removal algorithm for binary document images. The proposed approach aims at understanding script-independent prominent text component features using supervised classification as a first step. It then uses their cohesiveness and stroke-width properties to filter and associate smaller text components with them using an unsupervised classification...
The paper presents a clutter detection and removal algorithm for complex document images. The distance transform based approach is independent of clutter's position, size, shape and connectivity with text. Features are based on a residual image obtained by analysis of the distance transform and clutter elements, if present, are identified with an SVM classifier. Removal is restrictive, so text attached...
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