A Novel Handwritten Urdu Word Spotting Based on Connected Components Analysis

Sagheer, Malik Waqas; Nobile, Nicola; He, Chun Lei; Suen, Ching Y.

doi:10.1109/icpr.2010.496

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…The lexicon of 57 Urdu words and 44 Urdu characters mainly comprises financial terms to support recognition of offline Urdu words, characters and digits. This is the first published database on Urdu handwriting and has been employed in recognition and spotting of Urdu handwritten words [165,166].…”

Section: Cenparmi Urdu 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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Section: Cenparmi Urdu 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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“…Besides, hybrid models of HMMs with different supervised learning methods have been developed that combine HMMs with support vector machine (SVM) [41] or with neural network (NN) [42] or with deep convolutional neural network (CNN) [43]. In another work, a new word spotting system for handwritten Urdu language document images is proposed [44]. The method uses several pre-processing steps such as binarization, connected component analysis and edge detection.…”

Section: Word/pattern Spottingmentioning

confidence: 99%

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

Cheddad

Kusetoğulları

Hilmkil³

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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“…Sagheer et al [27] proposed an Urdu word spotting system that was evaluated using the Urdu handwritten documents database of the proposed sets. The system reached a 92.11% correct segmentation rate, a 50.75% precision rate and a 70.1% recall rate for word spotting.…”

Section: B Experiments On Handwritten Documentsmentioning

confidence: 99%

Novel Handwritten Words and Documents Databases of Five Middle Eastern Languages

Nobile

Khayyat

Lam

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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This paper introduces new handwritten databases of selected words in the five Middle-Eastern languages of Arabic, Dari, Farsi, Pashto and Urdu. The databases share a common lexicon of forty words that are related to finance and are used in daily life. The five databases have been collected from over 1600 native writers located in four countries. Recognition results for each of the databases are also presented. Results come from three classifiers (Support Vector Machines, Modified Quadratic Discriminant Function. and Multi-layer Perceptron) which were implemented for recognition of the words based on gradient features. Given the diversity of the data, the results demonstrate the effectiveness of the implemented process in learning and recognizing samples of handwritten words from different languages.In addition, full page handwritten documents of each language are presented, with approximately forty pages per language. Each document has associated groundtruth information.

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A Novel Handwritten Urdu Word Spotting Based on Connected Components Analysis

Cited by 13 publications

References 8 publications

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

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

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

Novel Handwritten Words and Documents Databases of Five Middle Eastern Languages

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