Numeral Script Identification from Handwritten Document Images

Obaidullah, Sk Md; Halder, Chayan; Das, Nibaran

doi:10.1016/j.procs.2015.06.067

Cited by 22 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Obaidullah et al [151] proposed a handwritten numeral script identification (HNSI) framework to identify numeral text written in Bangla, Devanagari, Roman and Urdu. The framework is based on a combination of daubechies wavelet decomposition [152] and spatial domain features.…”

Section: Urdu Languagementioning

confidence: 99%

“…The results showed that the highest recognition rate of 98.5% was achieved. Obaidullah et al [151] proposed Handwritten Numeral Script Identification or HNSI framework based on four indic scripts, namely, Bangla, Devanagari, Roman and Urdu. The researchers used different classifiers, namely NBTree, PART, Random Forest, SMO, Simple Logistic and MLP and evaluated the performance against the true positive rate.…”

Section: F Indian Scriptmentioning

confidence: 99%

See 1 more Smart Citation

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

359

View full text Add to dashboard Cite

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

show abstract

Section: Urdu Languagementioning

confidence: 99%

Section: F Indian Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

359

View full text Add to dashboard Cite

show abstract

“…So, designing a handwritten numeral recognizer which can work in a multilingual environment will be of huge significance in the Indian context. A lot of research work (Chatterjee et al, 2019;Hangarge et al, 2013;Obaidullah et al, 2019;Pardeshi et al, 2014;Sahare et al, 2019;Singh et al, 2019;Singh, Sarkar, Bhateja, & Nasipuri, 2018;Singh, Sarkar, Nasipuri, & Doermann, 2015) has already been done for the recognition of characters (considering only text), but only a few work (Obaidullah et al, 2015;Obaidullah et al, 2016;Rakshit et al, 2019;Roy et al, 2004) has been extended to numerals. Motivated by the research gap, our approach aims to recognize handwritten numerals in four major scripts used in the Indian sub-continent.…”

Section: Existing Workmentioning

confidence: 99%

A new feature extraction approach for script invariant handwritten numeral recognition

et al. 2021

View full text Add to dashboard Cite

Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi-lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script-invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

show abstract

“…The last posterior probability will be used as the prior probability of another adjustment in order to get the new posterior probability. This is a continuously-renewed and repeatedly-adjusted process (Obaidullah et al, 2015;Baoping and Yonghong et al, 2013).…”

Section: Representation Of Bayesian Networkmentioning

confidence: 99%

A Handwritten Digit Recognition Method Based on Bayesian Network

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

View full text Add to dashboard Cite

Handwritten digit recognition is a classical pattern recognition problem and it has significant research and application value. Bayesian network is an effective tool to apply probability and statistics into uncertainty reasoning and data analysis of complicated system. On the basis of the analysis of the classification and connection weight design method of Bayesian network model, this paper learns and researches the structure learning and parameter learning method of Bayesian network in both complete data and incomplete data, proposes the Bayesian network classifier to be applied in handwritten digit recognition and builds the Bayesian network model for handwritten digit model. This network model uses the training sample set, identifies the topological structure of Bayesian network by integrating as much prior knowledge as possible and determine the conditional probability table for various variables in the given network structure. The experiment results proves that the Bayesian network classifier built in this paper has excellent effect and the sample training classifier improves the classification efficiency to a large extent and it can accurately reject the counter-example and reduce the false accept rate.

show abstract

Numeral Script Identification from Handwritten Document Images

Cited by 22 publications

References 16 publications

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

A new feature extraction approach for script invariant handwritten numeral recognition

A Handwritten Digit Recognition Method Based on Bayesian Network

Contact Info

Product

Resources

About