Intelligent character recognition using fully convolutional neural networks

Ptucha, Raymond; Such, Felipe Petroski; Pillai, Suhas; Brockler, Frank; Singh, Vatsala; Hutkowski, Paul

doi:10.1016/j.patcog.2018.12.017

Cited by 140 publications

(74 citation statements)

References 14 publications

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“…These pixels are fed as input to multiple hidden layers for feature extraction and a connected layer, which detects and classifies object [55]. A recent study by [69] used fully convolutional neural network(FCNN) on IAM and RIMES datasets. Results were promising, and researchers achieved the character error rate(CER) and word error rate(WER) of 4.7%, 8.22%, 2.46%, 5.68% respectively.…”

Section: A English Languagementioning

confidence: 99%

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

Memon

Sami

Khan³

et al. 2020

IEEE Access

359

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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%

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

“…The authors considered different univariate measures to produce a feature ranking and proposed a greedy search approach for choosing the feature subset able to maximize the classification results. Raymond et al [ 30 ] presented a fully convolutional network architecture that outputs arbitrary length symbol streams from handwritten text. A preprocessing step normalizes input blocks to a canonical representation, which negates the need for costly recurrent symbol alignment correction.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

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“…With the rapid development of computer science and technology, artificial intelligence (AI) is increasingly prevalent in our daily life. Among others, deep neural network (DNN) has been successfully utilized in a variety of tasks (Hinton et al, 2012;Jurgen, 2015;Liu et al, 2016;Ptucha et al, 2019;Zeng et al, 2018), e.g. character recognition, image recognition, and speech recognition, etc.…”

Section: Introductionmentioning

confidence: 99%

Human face recognition based on convolutional neural network and augmented dataset

Song

2020

Systems Science & Control Engineering

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To deal with the issue of human face recognition on small original dataset, a new approach combining convolutional neural network (CNN) with augmented dataset is developed in this paper. The original small dataset is augmented to be a large dataset via several transformations of the face images. Based on the augmented face image dataset, the feature of the faces can be effectively extracted and higher face recognition accuracy can be achieved by using the ingenious CNN. The effectiveness and superiority of the proposed approach can be verified by several experiments and comparisons with some frequently used face recognition methods.

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Intelligent character recognition using fully convolutional neural networks

Cited by 140 publications

References 14 publications

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

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

Recognition of Pashto Handwritten Characters Based on Deep Learning

Human face recognition based on convolutional neural network and augmented dataset

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