Seam carving-based Arabic handwritten sub-word segmentation. Issue 1 (1st January 2020) - Berriche, Lamia; Al-Mutairy, Abeer
Abstract: Collecting, archiving, and analyzing handwritten documents are still indispensable acts as people continue to use paper documents. In addition, these archives are an endless source of information. Retrieving information from a handwritten document using automatic text queries as an alternative to manual search in a scanned image requires precise optical character recognition systems. However, properties of the Arabic handwriting such as cursive writing with characters overlapping and touching limit the recognition stages. Inspired by the use of the seam carving algorithm for image resizing and line segmentation, we propose a seam carving algorithm that aims to solve character overlapping and touching. We propose a new energy function for the seam carving algorithm to solve the overlapping problem. Furthermore, we designed a pre-processing stage that avoids generating seams that pass through inappropriate regions. Our proposed segmentation technique reached a 95.66% correct segmentation on IESK-ArDB and IFN/ENIT datasets. Furthermore, our method outperforms state-of-the-art techniques in resolving the overlapping issue.