A methodology to spot words in historical Arabic documents

Zirari, Fattah; Ennaji, Abdellatif; Nicolas, Stéphane; Mammass, Driss

doi:10.1109/aiccsa.2013.6616492

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…4.2, the increase in the performance is significant. We also use Run Length Smoothing Algorithm (RLSA) [45,66] as another baseline to compare our proposed method, and have seen that RLSA is not able to capture the boundaries sufficiently as well (see Table 3 and Fig. 9).…”

Section: Results Of Word Segmentation In the Target Datasetmentioning

confidence: 99%

Cross-document word matching for segmentation and retrieval of Ottoman divans

Duygulu

Arifoǧlu

Kalpaklı

2014

Pattern Anal Applic

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Cataloged from PDF version of article.Motivated by the need for the automatic\ud indexing and analysis of huge number of documents in\ud Ottoman divan poetry, and for discovering new knowledge\ud to preserve and make alive this heritage, in this study we\ud propose a novel method for segmenting and retrieving\ud words in Ottoman divans. Documents in Ottoman are dif-\ud ficult to segment into words without a prior knowledge of\ud the word. In this study, using the idea that divans have\ud multiple copies (versions) by different writers in different\ud writing styles, and word segmentation in some of those\ud versions may be relatively easier to achieve than in other\ud versions, segmentation of the versions (which are difficult,\ud if not impossible, with traditional techniques) is performed\ud using information carried from the simpler version. One\ud version of a document is used as the source dataset and the\ud other version of the same document is used as the target\ud dataset. Words in the source dataset are automatically\ud extracted and used as queries to be spotted in the target\ud dataset for detecting word boundaries. We present the idea\ud of cross-document word matching for a novel task of\ud segmenting historical documents into words. We propose a\ud matching scheme based on possible combinations of\ud sequence of sub-words. We improve the performance of\ud simple features through considering the words in a context.\ud The method is applied on two versions of Layla and\ud Majnun divan by Fuzuli. The results show that, the proposed\ud word-matching-based segmentation method is\ud promising in finding the word boundaries and in retrieving\ud the words across documents

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Section: Results Of Word Segmentation In the Target Datasetmentioning

confidence: 99%

Cross-document word matching for segmentation and retrieval of Ottoman divans

Duygulu

Arifoǧlu

Kalpaklı

2014

Pattern Anal Applic

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“…There are very few Arabic handwriting keyword spotting studies in the literature [6]. Some QbE studies ( [18][19][20]) are proposed for the historical Arabic documents and used a matching method adjusted to the Arabic script. QbS approaches [21,22] used the HMM technique for keyword spotting in handwritten Arabic manuscripts.…”

Section: Related Workmentioning

confidence: 99%

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Can

Kabadayı

2020

Applied Sciences

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Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

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“…For both the aforementioned strategies, the core operational pipeline used relies upon two main components: features extraction and matching. Although there is a plethora of systems that are tailored for both modern [4,5] and historical machine-printed documents [6][7][8][9], in the case of handwritten documents, very few of them can be used effectively due to severe degradation, text layout complexity and variation in the writing style.…”

Section: Introductionmentioning

confidence: 99%

Word Spotting as a Service: An Unsupervised and Segmentation-Free Framework for Handwritten Documents

2021

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Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits efficient and effective word spotting in handwritten documents is presented that relies upon document-oriented local features that take into account information around representative keypoints and a matching process that incorporates a spatial context in a local proximity search without using any training data. The method relies on a document-oriented keypoint and feature extraction, along with a fast feature matching method. This enables the corresponding methodological pipeline to be both effectively and efficiently employed in the cloud so that word spotting can be realised as a service in modern mobile devices. The effectiveness and efficiency of the proposed method in terms of its matching accuracy, along with its fast retrieval time, respectively, are shown after a consistent evaluation of several historical handwritten datasets.

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A methodology to spot words in historical Arabic documents

Cited by 9 publications

References 12 publications

Cross-document word matching for segmentation and retrieval of Ottoman divans

Cross-document word matching for segmentation and retrieval of Ottoman divans

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Word Spotting as a Service: An Unsupervised and Segmentation-Free Framework for Handwritten Documents

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