Handwritten Numeral Recognition of Six Popular Indian Scripts

Pal, Umapada; Wakabayashi, Tetsushi; Sharma, Nabin; Kimura, Fumitaka

doi:10.1109/icdar.2007.4377015

Cited by 126 publications

(51 citation statements)

References 8 publications

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“…Very few works were found on handwritten Oriya numerals [1] [2] [3]. In this section, we present some works related to handwritten Oriya numerals recognition using different techniques and datasets carried out by different researchers followed by a summary on works done on other Oriya characters.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Isolated Handwritten Oriya Numerals using Hopfield Neural Network

Sarangi¹,

Sahoo²,

Ahmed³

2012

IJCA

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Designing an automatic pattern recognition system is a challenging task. However, despite the design challenges, its enormous application potentials have attracted the attention of researchers and developers over the last four to five decades. Design of recognition systems for handwritten character applications has been a subject of intensive research, and the search is still on for a robust technique capable of dealing with natural variations in handwritten characters. In this paper the performance of Hopfield neural network (HNN) model in recognizing the handwritten Oriya (an Indian language) digits is addressed. The implementation has been carried out in two different ways. In first case, 290 test patterns (29 elements of each classes 0-9) created by different persons in Microsoft Paint were presented to the network in image form of 12×12. It is found that the network recognized 97.95% of the input characters correctly even if 40% of the input characters were having a significant level of noise. In the second experiment, the inputs were the collected handwritten characters in image format.A total of 1500 different input patterns were fed to the network sequentially and 95.4% recognition accuracy is achived. All the activities such as preprocessing of data (image cropping, resizing , digitization and implementations) have been carried out using MATLAB.

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Section: Related Workmentioning

confidence: 99%

Recognition of Isolated Handwritten Oriya Numerals using Hopfield Neural Network

Sarangi¹,

Sahoo²,

Ahmed³

2012

IJCA

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“…Chain code and gradient-based features are used for Devanagari numeral recognition. Even for recognizing Devanagari handwritten characters, the method proposed by Pal et al [19] has the highest accuracy.…”

Section: Post-processingmentioning

confidence: 99%

A Review on Offline Handwritten Recognition of Devnagari Script

Patwardhan¹,

Deshmukh²

2015

IJCA

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“…We achieved recognition rates of up to 89% on word level using the skeleton based method for baseline estimation and skeleton direction features. [86] Although, many pieces of work have been done for the recognition of printed characters in Indian languages, only a few attempts have been made towards the recognition of handwritten characters [87][88][89] have presented a Devanagari numeral recognition system in which the presence or absence of four basic primitives, namely, horizontal and vertical line segments, right and left slant, and their interconnections are used for the recognition with a decision tree.…”

Section: Pre-processing:-mentioning

confidence: 99%