An efficient Optical Character Recognition algorithm using artificial neural network by curvature properties of characters

Farhad, M. M.; Hossain, S. M. Nafiul; Khan, Arshad; Islam, Atiqul

doi:10.1109/iciev.2014.6850844

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…In addition, calculation are burden with unnecessary pixel description. Less complex, the FD methods [3] [4] [5] perform the description of characters based on some specific Features.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, pixel comparison in matrix matching is extremely sensitive to noisy characters [1] [2]. This stage can be performed using classification methods such as RNA, SVM [3][4] [5]. They give interesting results, however they still complex comparing to matching process.…”

Section: Introductionmentioning

confidence: 99%

“…They give interesting results, however they still complex comparing to matching process. Indeed, simple feature matching ensures a good trade off between accuracy and computation complexity [5].…”

Section: Introductionmentioning

confidence: 99%

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Efficient multiscale and multifont optical character recognition system based on robust feature description

Soua

Kachouri

Akil

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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International audienceOptical Character Recognition (OCR) is the process of translating images of text into a comprehensible machine format. Generally, an OCR system is composed of binariza-tion, segmentation and recognition stages. Given an extracted binary character, the recognition stage ensures its description and decides its corresponding ASCII code. In this paper, we propose a new OCR system that aims to high speed, Multiscale and Multifont character recognition. Our proposal is based essentially on robust description using a new Unified Character Descriptor (UCD). In addition, a character type-face and font-size recognition is performed to choose the adequate template for faster matching process. Obtained OCR Accuracy of our proposed System is 1.5x higher then that reached by Tesseract on the LRDE dataset

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“…In addition, calculation are burden with unnecessary pixel description. Less complex, the FD methods [3] [4] [5] perform the description of characters based on some specific Features.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient multiscale and multifont optical character recognition system based on robust feature description

Soua

Kachouri

Akil

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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show abstract

“…It is observed that poor visibility acts as a barrier for correct OCR process. The accuracy of OCR and classification can further be enhanced by including other features like estimating the curve angles of characters [6]. We anticipate that although this approach produces better results, improved algorithms will be needed to improve the computational time.…”

Section: E Graphical User Interface (Gui) Developmentmentioning

confidence: 99%

“…M. Farhad et al [6] proposed a novel methodology for OCR of English alphabets using ANN as the classification algorithm with curvature features of characters as input to the network. They used different seeking angles for the recognition of characters considering the predetermined features of them.…”

Section: IImentioning

confidence: 99%

Towards Development of Real-Time Handwritten Urdu Character to Speech Conversion System for Visually Impaired

Sultana¹,

Abbasi²,

Usmani³

et al. 2016

ijacsa

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Abstract-Text to Speech (TTS) Conversion Systems havebeen an area of research for decades and have been developed for both handwritten and typed text in various languages. Existing research shows that it has been a challenging task to deal with Urdu language due to the complexity of Urdu 'Nastaliq' (rich variety in writing styles), therefore, to the best of our knowledge, not much work has been carried out in this area. Keeping in view the importance of Urdu language and the lack of development in this domain, our research focuses on 'handwritten' Urdu TTS system. The idea is to first recognize a handwritten Urdu character and then convert it into an audible human speech. Since handwriting styles of different people vary greatly from each other, a machine learning technique for the recognition part is used i.e., Artificial Neural Networks (ANN). Correctly recognized characters, then, undergo processing which converts them into human speech. Using this methodology, a working prototype has been successfully implemented in MATLAB that gives an overall accuracy of 91.4%. Our design serves as a platform for further research and future enhancements for word and sentence processing, especially for visually impaired people.

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Text Attentional Character Detection Using Morphological Operations: A Survey

Kumar

Divya

Dharshni

et al. 2020

New Trends in Computational Vision and Bio-Inspired Computing

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An efficient Optical Character Recognition algorithm using artificial neural network by curvature properties of characters

Cited by 10 publications

References 12 publications

Efficient multiscale and multifont optical character recognition system based on robust feature description

Efficient multiscale and multifont optical character recognition system based on robust feature description

Towards Development of Real-Time Handwritten Urdu Character to Speech Conversion System for Visually Impaired

Text Attentional Character Detection Using Morphological Operations: A Survey

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