Hindi character recognition using RBF neural network and directional group feature extraction technique

Singh, Dayashankar; Saini, Jitender; Chauhan, D. S.

doi:10.1109/ccip.2015.7100726

Cited by 20 publications

(11 citation statements)

References 9 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Radial Basis Function (RBF) with one input layer and one output layer has been used to train RBF network. As compared to back propagation neural network, gradient feature extraction resulted in less accuracy with RBF using directional group values [47]. OCR systems perform better for scanned documents but different variation in images have shown inappropriate results [137].…”

Section: Text Recognitionmentioning

confidence: 99%

An analytical study of information extraction from unstructured and multidimensional big data

2019

View full text Add to dashboard Cite

IntroductionInformation extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The extracted information from unstructured data is used to prepare data for analysis. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. Numerous techniques have been introduced for different data types i.e. text, image, audio, and video.The advancement in technology promoted the rapid growth of data volume in recent years. The volume, variety (structured, unstructured, and semi-structured data) and velocity of big data have also changed the paradigm of computational capabilities of the systems. IBM estimated that more than 2.5 quintillion bytes of data are generated every Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adnan and Akbar J Big Data (2019) 6:91 Malaysia Adnan and Akbar J Big Data (2019) 6:91 RESEARCHday. Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years. IDC estimated that unstructured data will be 95% of the global data in 2020 with estimated 65% annual growth rate [1]. The common characteristics of unstructured data are, (i) it comes in multiple formats...

show abstract

Section: Text Recognitionmentioning

confidence: 99%

An analytical study of information extraction from unstructured and multidimensional big data

2019

View full text Add to dashboard Cite

show abstract

“…After successful completion all the above step the primary feature set is drawn, and all these are forwarded to the Radial Basic Function Neural Network (RBFNN) [21] classifier for classification. The RBFNN is basically takes a real valued function which calculates the distance from some specific point and it must satisfy the property of radial function [22] in the classification problem.…”

Section: Classification Using Radial Basic Function Neural Networkmentioning

confidence: 99%

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

2019

IJITEE

View full text Add to dashboard Cite

Handwritten Character Recognition is most challenging area of research, in which for various aspects a little enhancement can be always achieved. It is due to the irregularity of writing and shapes of different class user’s orientation affects the recognition rate. In this paper we have taken the complexity of Odia handwritten character recognition and successfully resolve with Principal Component Analysis (PCA). Here we had adopted a model in which the importance of symmetric axis chords in recognition of unconstrained handwritten characters is established. This symmetric axis chords are drawn along both row-wise and column-wise among the points one end to other. In addition to we have calculated the statistical feature as Euclidian distance, Hamilton distance which drawn from the midpoint of the symmetric chord to nearest pixel of the character. Apart from it we have also reported the angular values from the centroid of the image to the character pixel. This empirical model also harnessed the PCA over the feature set and perform the dimension reduction to the feature set which later termed as the key feature set. A certain series of experiment was carried on for the proper implementation of proposed technique, henceforth we have taken the standard Handwritten Database from various research institutes. Lastly on simulation analysis Radial Basis Function Neural Network (RBFNN) has been reported as to achieve high recognition rate through Gaussian kernel and a comparison among them has also reported here with.

show abstract

“…We have listed up of the key feature vector of respective images at certain frequencies then forwarded to the classifier to perform the classification. In this paper we have considered a RBF neural network [19] as the classifier. As per the nature of neural network with this we have just added some basis function to it and also used predict the behavior of the kernel function associated with the neural network [20].…”

Section: Classification Phasementioning

confidence: 99%

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Sethy¹,

Patra²,

Nayak³

et al. 2018

IJET

View full text Add to dashboard Cite

Optical Character Recognition is one of the most interesting and highly motivated areas of research, which has been very much ap-preciated in different aspect to the area of digitations world. Here in this paper we have suggested a probabilistic approach for develop-ing recognition system for handwritten Odia numerals. To report a good level of recognition of Odia scripts is quite challenging with respect to other Indian scripts .All the procedure are sequentially enclosed to develop an recognition model and report a successful recognition accuracy. Here we have performed the analysis over to standard handwritten numeral database named as IITBBS Odia Numeral Database, which is collected from IIT Bhubaneswar. In the suggestive recognition system we have adopted a 2D-Gabor wavelet transformation approach for selection of feature vector. Apart from it we have also noted down the dimensional reduction to the obtained feature vector by sustaining to PCA. In order to predict high recognition rate we have followed up by RBF Neural Network classifier. In addition to it we have also evaluate different version of RBF like Gaussian and Polynomial. Performing over 400 samples each of 10 categories (400*10) number of Odia numeral images, we have maintained a well-defined training and testing ratio in the clas-sifier and achieved 98.02%, 96.8%.recognition rate for the reported classifiers.

show abstract

Hindi character recognition using RBF neural network and directional group feature extraction technique

Cited by 20 publications

References 9 publications

An analytical study of information extraction from unstructured and multidimensional big data

An analytical study of information extraction from unstructured and multidimensional big data

Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Contact Info

Product

Resources

About