Arabic handwritten word spotting using language models

Khayyat, Muna; Lam, Louisa; Suen, Ching Y.

doi:10.1109/icfhr.2012.183

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…The third set is a combination of samples from set 1 and set 2. The database has been used for recognition of Arabic characters [140] and numerals [141] as well as word spotting [142]. …”

Section: Cenparmi Arabic Handwriting Databasementioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

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“…The third set is a combination of samples from set 1 and set 2. The database has been used for recognition of Arabic characters [140] and numerals [141] as well as word spotting [142]. …”

Section: Cenparmi Arabic Handwriting Databasementioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…In a recent segmentation-free word spotting approach for Arabic documents [28,29], the authors propose a learning-based word spotting system. For the first time in the literature of Arabic word spotting, language models were integrated with the partial segmentation of the words, to represent contextual information and reconstruct words.…”

Section: Related Workmentioning

confidence: 99%

“…Most of these techniques require word segmentation before searching for a word [6,7,37]. Although there are some segmentation-free approaches [1,17,20,28,29], their computational cost is usually high. Thus, providing a word segmentation schema would be beneficial and make the searching processes easier and faster.…”

Section: Related Workmentioning

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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“…Word encoding techniques [30][31][32][33] transform words into numbers and texts into number vectors. The index-based word encoding is the most frequently used encoding technique which is the first step to create a dictionary that matches words to indexes.…”

Section: Word Encodingmentioning

confidence: 99%

Epilepsy Radiology Reports Classification Using Deep Learning Networks

Bayrak¹,

Yücel²,

Takçı³

2022

Computers, Materials &Amp; Continua

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The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given text report our systems first cleans HTML/XML tags, tokenize, erase punctuation, normalize text, (ii) then it converts into MRI text reports numeric sequences by using indexbased word encoding, (iii) then we applied the deep learning models that are uni-directional long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network and convolutional neural network (CNN) for the classifying comparison of the data, (iv) finally, we used 70% of used for training, 15% for validation, and 15% for test observations. Unlike previous methods, this study encompasses the following objectives: (a) to extract significant text features from radiologic reports of epilepsy disease; (b) to ensure successful classifying accuracy performance to enhance epilepsy data attributes. Therefore, our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models. The traditional method is numeric sequences by using index-based word encoding which has been made for the first time in the literature, is successful feature descriptor in the epilepsy data set. The BiLSTM network has shown a promising performance regarding the accuracy rates. We show that the larger sized medical text reports can be analyzed by our proposed method.

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Arabic handwritten word spotting using language models

Cited by 12 publications

References 7 publications

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

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

Epilepsy Radiology Reports Classification Using Deep Learning Networks

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