A large amount of handwritten historical documents are located in libraries around the world. The desire to access, search, and explore these documents paves the way for a new age of knowledge sharing and promotes collaboration and understanding between human societies. Currently, the indexes for these documents are generated manually, which is very tedious and time consuming. Results produced by state of the art techniques, for converting complete images of handwritten documents into textual representations, are not yet sufficient. Therefore, word-spotting methods have been developed to archive and index images of handwritten documents in order to enable efficient searching within documents. In this paper, we present a new matching algorithm to be used in word-spotting tasks for historical Arabic documents. We present a novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts. Matching results are used to cluster images of Arabic word-parts into different classes using the Nearest Neighbor rule. To compute the distance between two word-part images, the algorithm subdivides each image into equal-sized slices (windows). A modified version of the Chamfer Distance, incorporating geometric gradient features and distance transform data, is used as a similarity distance between the different slices. Finally, the Dynamic Time Warping (DTW) algorithm is used to measure the distance between two images of word-parts. By using the DTW we enabled our system to cluster similar word-parts, even though they are transformed non-linearly due to the nature of handwriting. We tested our implementation of the presented methods using various documents in different writing styles, taken from Juma'a Al Majid Center -Dubai, and obtained encouraging results.
In REF they embedded a modified Chamfer Distance based on geometric gradient features into DTW.
Word spotting for handwritten documents using Chamfer Distance and Dynamic Time Warping
Electronic Imaging
Engineering Computer Science