Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique

Choudhary, Amit; Rishi, Rahul; Ahlawat, Savita

doi:10.1016/j.aasri.2013.10.045

Cited by 52 publications

(14 citation statements)

References 4 publications

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“…In 2007 Manjunath Aradhya et al [13] showed a multi-lingual OCR leading to a good accuracy. Afterwards, Desai [14] used OCR technique for recognizing Gujrati handwritten digits in 2010 resulting in 82 % success rate and likewise Choudhary et al [15] routine this with binarization method to judge capability of OCR for english characters in 2013 giving accuracy of 85.62 %.…”

Section: Related Workmentioning

confidence: 98%

Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language

Gautam

Sharma

Hazrati

2016

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2

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Handwriting recognition and OCR are two major fields of recognition and classification, one is area and other is dawn. Archaeology is that field of study where recognition and classification is needed at most to recognize ancient artefacts written in languages like Pali having various scripts such as Khmer, Sinhala, Devanagari and more. Handwritten character recognition with MOCR (Modified OCR) is shown for Pali language in this paper portraying modified OCR to recognize Brahmi script and showing comparisons in terms of accuracy with two other scripts that are Akkhara-Muni and Ariyaka. Brahmi giving an overall success rate of 85.66, 85.73 and 88.83 % respectively. MOCR has new steps in various phases which results in better accuracy than OCR.

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Section: Related Workmentioning

confidence: 98%

Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language

Gautam

Sharma

Hazrati

2016

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2

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“…This technique yields ~90% accuracy for different character seeking angles but it was computationally expensive as feature extraction proves to be time consuming. www.ijacsa.thesai.org Amit Choudhary et al [7] presented an extensive work on offline handwritten English character recognition using multilayer feed forward neural network reporting an accuracy of 85.62%. Ganai et al [8] focused on improving the speech quality of existing system by combining the methods of Hidden Markov Model (HMM)-based speech synthesis and waveform-based speech synthesis to develop human like speech.…”

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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“…Such forward network with hidden layer(s) has an important feature that the units in the hidden layer(s) can randomly form their own input presentation and the weights between the units of the input layer and the hidden layer determines whether each unit in the hidden layer is activated. BP algorithm is slow in learning and the predictive ability training capability (learning capability) of its network is contradictory (Parshuram and Ravinda, 2015;Amit and Rahul et al, 2013). The structure of BP network is shown as Fig.7.…”

Section: Bp Algorithmmentioning

confidence: 99%

Character Recognition Algorithm Based on Neural Network

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Character recognition is a topic with great research value and it includes all typical issues in the field of pattern recognition: data acquisition, processing and selection, selection of input sample expression as well as the guided training on the recognizer with sample set. Artificial neural network (ANN) is a mathematical model which simulates the behavior characteristics of neural network and performs distributed and parallel information processing. Along with the progress in neural network and logic techniques, people have used many new methods and means on the study of character recognition. This paper proposes a character recognition method based on improved BP neural network. This method extracts features of training samples according to certain methods, builds the classification analysis system, divides each character to be recognized into 8*8 blocks for digitalization and represents each block with a vector. The input vector is defined as an input vector matrix and the vector represents a certain character or number. Then, it conducts feature extraction on certain unknown sample to be detected with the same method and the system will judge its classification according to the sample feature extracted. The training of neural network shall train the arrays which represent characters or numbers respectively in the input end under supervision and correspond to the specific location in the output end. The test result on this character recognition method shows that this algorithm has relatively high recognition accuracy, a faster processing speed and strong robustness in the applications.

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Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique

Cited by 52 publications

References 4 publications

Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language

Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language

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

Character Recognition Algorithm Based on Neural Network

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