Abstract
Spotting keywords in modern handwritten documents is an interesting problem that allows to search, index, and classify document images. This paper investigates the word spotting problem in a segmentation-based framework where features extracted from word images are employed to match a query keyword with those in a reference base. More specifically, we employ oriented basic image features (oBIFs) to characterize the word images while matching is carried out by computing the distance (similarity) between word images in the feature space. Experimental study of the system is carried out using the dataset of the ICFHR 2014 word spotting competition and promising results are reported in terms of P@K precision and mean average precision (mAP).
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Yousfi, D., Gattal, A., Djeddi, C., Siddiqi, I., Bensefia, A. (2022). Keyword Spotting in Modern Handwritten Documents Using oBIFs. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_18
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DOI: https://doi.org/10.1007/978-3-031-04112-9_18
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