Word of blobs

In this paper, we present a novel scheme for subdividing a pictorial representation of a word or word-part into a sequence of blobs, that resemble the stroke representing the word. These blobs are generated by applying a bank of Gabor filters that capture the width of the strokes in multiple directions and segment the strong response regions. From the resulting blobs we extract representative features that are combined using bag-of-features. The proposed scheme is robust; i.e., insensitive to noise, and works directly on gray scale images. It represents the handwritten curves as a sequence of elliptic blobs, whose width is similar to that of the original handwriting. We incorporated the proposed approach in word spotting procedure and evaluated its performance on Arabic handwritten datasets.