Off-line Handwritten Numeral Recognition using Hybrid Feature Set – A Comparative Analysis

Ahlawat, Savita; Rishi, Rahul

doi:10.1016/j.procs.2017.11.478

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…We believe that to efficiently recognize handwritten and machine printed text of the English language, researchers have used almost all of the available feature extraction and classification techniques. These feature extraction and classification techniques include but not limited to HOG [130] , bidirectional LSTM [131], directional features [132], multilayer perceptron (MLP) [119], [133], [134], hidden markov model(HMM) [26], [52], [54], [62], Artificial neural network (ANN) [135]- [137] and support vector machine (SVM) [29], [67].…”

Section: A English Languagementioning

confidence: 99%

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

Memon

Sami

Khan³

et al. 2020

IEEE Access

325

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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.

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Section: A English Languagementioning

confidence: 99%

“…multilayer perceptron classifier gave better accuracy on Devanagri, and Bangla numerals [25], [140] but gave average results for other languages [? ], [133]. The difference may have been due to the fact of how specific technique models a different style of characters and quality of the dataset.…”

Section: Conclusion and Future Work A Conclusionmentioning

confidence: 99%

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

Memon

Sami

Khan³

et al. 2020

IEEE Access

325

View full text Add to dashboard Cite

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“…HTR can be performed both online and offline. The former corresponds to a workflow with a reader that needs to understand a handwritten message in real time (Ahlawat and Rishi, 2017). For example: recognizing numbers written on bank checks or options on manually filled in forms.…”

Section: Related Workmentioning

confidence: 99%

End-to-end platform evaluation for Spanish Handwritten Text Recognition

Xamena

Barboza

Orozco

2021

CyT

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The task of automated recognition of handwritten texts requires various phases and technologies both optical and language related. This article describes an approach for performing this task in a comprehensive manner, using machine learning throughout all phases of the process. In addition to the explanation of the employed methodology, it describes the process of building and evaluating a model of manuscript recognition for the Spanish language. The original contribution of this article is given by the training and evaluation of Offline HTR models for Spanish language manuscripts, as well as the evaluation of a platform to perform this task in a complete way. In addition, it details the work being carried out to achieve improvements in the models obtained, and to develop new models for different complex corpora that are more difficult for the HTR task.

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“…In [12], A. Desai proposed a multi-layered feed forward neural network for identification and classification of Gujarati numerals. [13] reports a hybrid feature set obtained using different feature extraction approaches and neural network classifier for recognition of handwritten numerals.…”

Section: Literature Reviewmentioning

confidence: 99%

Handwritten English Digit Recognition: A Machine Learning Formulation

Behera*,

Das

2019

IJRTE

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Handwriting recognition is a challenging machine learning task. Handwritten Recognition (HR) systems have become commercially popular due to their potential applications. The challenges that arise due to wide range of variations in shape, structure ,size and individual writing style can be handled with the combination of a powerful feature extraction technique and an efficient classifier. In this paper, an attempt has been made to compare four different feature extraction cum classifier schemes for English handwritten numeral recognition in terms of computational time and accuracy of recognition. Observations show that single decision tree requires less computation time while SVM yields better accuracy.

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Off-line Handwritten Numeral Recognition using Hybrid Feature Set – A Comparative Analysis

Cited by 15 publications

References 13 publications

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

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

End-to-end platform evaluation for Spanish Handwritten Text Recognition

Handwritten English Digit Recognition: A Machine Learning Formulation

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