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EN
Binarisation methods already reported are inadequate for binarisation of complex documents such as maps due to large intensity variations across the regions and entangled texts with lines representing borders, rivers, roads and similar other components. This paper proposes a new binarisation technique for coloured land map images by extracting the regions and analysing the hue, saturation spread and within class kurtosis. This is a region-wise adaptive algorithm to cope up with the sharp changes of the discriminating features across different regions. Here, local regions are selected as clusters having the same hue and saturation. The regions are individually binarised using the spread of their degree of within class kurtosis and finally combined together. The regions extracted are further utilised for stitching of map documents which contain some portion in common. We use a simple greedy technique using correlation matching to join two or more map images such that information from both can be viewed and compared. Our experiments include 446 colour maps from the map image database created for this purpose and made freely available at website. This work is an extended version of our previous work on map image binarisation [1].
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