Segmentation-Free Keyword Retrieval in Historical Document Images

Rabaev, Irina; Dinstein, I.; El-Sana, Jihad; Kedem, Klara

doi:10.1007/978-3-319-11758-4_40

Cited by 5 publications

(1 citation statement)

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“…A word shape coding scheme is proposed by Bai et al [34] that combines feature descriptors and a matching technique for word spotting in document images. A block-based document image descriptor used for word spotting in historical printed documents based on the template matching process is proposed by Rabaev et al [35]. Their experiments show that this method provides promising results if the documents do not include too many undesired artefacts.…”

Section: Word/pattern Spottingmentioning

confidence: 99%

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

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et al. 2021

Neural Comput & Applic

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This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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Section: Word/pattern Spottingmentioning

confidence: 99%