HADARA &amp;#x2013; A Software System for Semi-Automatic Processing of Historical Handwritten Arabic Documents

Pantke, Werner; Märgner, Volker; Fecker, Daniel; Fingscheidt, Tim; Asi, Abedelkadir; Biller, Ofer; El-Sana, Jihad; Saabni, Raid; Yehia, Mohammad

doi:10.2352/issn.2168-3204.2013.10.1.art00036

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Learning‐free handwritten word spotting method for historical handwritten documents

Hassen

Subramanian

Al-Madeed

2021

IET Image Processing

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Word spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi-language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross-correlation measure, and the extracted ROIs are reranked using feature extraction and matching methods. The algorithm handles two types of scenarios: Segmentation-based and segmentation-free. It also facilitates the search for words that occur once as well as multiple times in the image. Evaluations were conducted on the George Washington and HADARA datasets using a standard evaluation method. The proposed methodology shows improved performance over contemporary technologies currently being used in the word spotting research field.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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