Neural based handwritten character recognition

Hanmandlu, M.; Mohan, Kala Raja; Kumar, Himanshu

doi:10.1109/icdar.1999.791769

Cited by 35 publications

(10 citation statements)

References 6 publications

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“…Among different branches of handwritten character recognition it is easier to recognize English alphabets and numerals than Tamil characters. Many researchers have also applied the excellent generalisation capabilities offered by ANNs to the recognition of characters [3], [4], [6]. Many studies have used fourier descriptors and Back Propagation Networks for classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Offline Tamil Handwritten Character Recognition System

Kishor

Yadav

Kumar³

2007

International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007)

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In this paper we propose an approach to recognize handwritten Tamil characters using a multilayer perceptron with one hidden layer. The feature extracted from the handwritten character is Fourier Descriptors. Also an analysis was carried out to determine the number of hidden layer nodes to achieve high performance of backpropagation network in the recognition of handwritten Tamil characters. The system was trained using several different forms of handwriting provided by both male and female participants of different age groups. Test results indicate that Fourier Descriptors combined with backpropagation network provide good recognition accuracy of 97% for handwritten Tamil characters.

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Section: Introductionmentioning

confidence: 99%

Neural Network Based Offline Tamil Handwritten Character Recognition System

Kishor

Yadav

Kumar³

2007

International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007)

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“…The backpropagation neural network is used in [11] for the recognition of handwritten characters whereas feature extraction is done using three different approaches, namely, ring, sector and hybrid. The features consist of normalized vector distances and angles.…”

Section: Introductionmentioning

confidence: 99%

Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals

Hanmandlu

Grover

Madasu

et al. 2007

Fourth International Conference on Information Technology (ITNG'07)

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This paper presents the recognition of HandwrittenHindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance features obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing the criterion function associated with the input fuzzy modeling. We then utilize a 'Reuse Policy' that provides guidance from past error values of the criteria function to accomplish the reinforcement learning. We will also show how the 'Reuse Policy' improves the speed of convergence of the learning process over other strategies that learn without reuse. There is a 25-fold improvement in training with the use of the reinforcement learning Experimentation is carried out on a limited database of around 3500 Hindi numeral samples. The overall recognition rate is found to be 95%.

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“…In the off-line recognition system, the neural networks have emerged as the fast and reliable tools for classification towards achieving high recognition accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Diagonal Feature Extraction Based Handwritten Character System Using Neural Network

Pradeep¹,

Srinivasan²,

Himavathi³

2010

IJCA

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A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form.

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Neural based handwritten character recognition

Abstract: This paper explores the existing ring based [2]

Cited by 35 publications

References 6 publications

Neural Network Based Offline Tamil Handwritten Character Recognition System

Neural Network Based Offline Tamil Handwritten Character Recognition System

Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals

Diagonal Feature Extraction Based Handwritten Character System Using Neural Network

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