A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition

Kumar, Munish; Jindal, M. K.; Sharma, Richa

doi:10.1007/s40009-014-0280-1

Cited by 32 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kumar et al [7] have presented efficient feature extraction techniques for offline handwritten Gurmukhi character recognition. They have also presented a hierarchical technique for offline handwritten Gurmukhi character recognition [5]. Using this technique, they accomplished a recognition accuracy of 91.8%.…”

Section: Introductionmentioning

confidence: 99%

Offline handwritten pre-segmented character recognition of Gurmukhi script

Kumar

Jindal

Sharma³

et al. 2016

MG&V

Self Cite

View full text Add to dashboard Cite

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

show abstract

Section: Introductionmentioning

confidence: 99%

Offline handwritten pre-segmented character recognition of Gurmukhi script

Kumar

Jindal

Sharma³

et al. 2016

MG&V

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kumar et al [24] used diagonal features, centroid features, horizontal peak extent and vertical peak extent features with hierarchical zoning (similar to proposed work) for offline handwritten Gurmukhi character recognition. They experimented with different combinations for features.…”

Section: Resultsmentioning

confidence: 99%

Devanagari ancient documents recognition using statistical feature extraction techniques

2019

Self Cite

View full text Add to dashboard Cite

Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system is designed for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters may include basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron, RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.

show abstract

“…The zoning feature extraction technique divides the character image into n number of regions in hierarchical arrangement as proposed by Kumar et al (2014). Suppose an input image is at current level L and then it is having 4 (L) sub-images.…”

Section: Feature Extractionmentioning

confidence: 99%

“…These feature extraction approaches have been evaluated by numerous researchers for distinct script identification, and it has been observed that these approaches are accomplishing recommendable results as compared to other approaches specified for handwriting recognition (Kumar et al 2013a, b). Initially, each input image is partitioned into n number of uniform sized zones for extracting various features (Kumar et al 2014). Depending on the four features specified, three classification techniques, namely, convolution neural network (CNN), decision tree and random forest are used for the classification of input characters.…”

Section: Introductionmentioning

confidence: 99%

Improved recognition results of offline handwritten Gurumukhi characters using hybrid features and adaptive boosting

et al. 2021

Self Cite

View full text Add to dashboard Cite

Offline handwritten character recognition is a part of the arduous area of research in the domain of document analysis and recognition. In order to enhance the recognition results of offline handwritten Gurumukhi characters, the authors have applied hybrid features and adaptive boosting approach in this paper. On feature extraction stage, zoning, diagonal, centroid, and peak extent-based features have been taken into account for extracting the meaningful information about each character. On the classification stage, three classifiers, namely decision tree, random forest, and convolution neural network classifier, are used. For experimental work, the authors have collected 14,000 pre-segmented samples of Gurumukhi characters (35-class problem) written by 400 writers where they have used 70% data as training set and remaining 30% data as testing set. The authors have also explored fivefold cross-validation technique for experimental work. The Ada-Boost approach along with the fivefold cross-validation strategy outstands the existing techniques in the relevant field with the recognition accuracy of 96.3%.

show abstract

A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition

Cited by 32 publications

References 18 publications

Offline handwritten pre-segmented character recognition of Gurmukhi script

Offline handwritten pre-segmented character recognition of Gurmukhi script

Devanagari ancient documents recognition using statistical feature extraction techniques

Improved recognition results of offline handwritten Gurumukhi characters using hybrid features and adaptive boosting

Contact Info

Product

Resources

About