Handwritten Script Recognition Using DCT, Gabor Filter, and Wavelet Features at Word Level

Rajput, G. G.; Anita, H. B.

doi:10.1007/978-81-322-1524-0_44

Cited by 9 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Almost all the methods either employed global or local and combinations of both global and local features. Primarily, global features used in [1]- [6] are based on DCT, DWT, Gabor, steerable pyramids, and the Radon transform. The local features, for example, shape features of connected components are employed in [7]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, they are weak in extracting directional energies of small size images, connected components, for instance. In particular, the DCT and wavelet features employed in [1] for classifying text blocks are not potential to sustain their performance in case of word image. The global method also suffers with the issues like time complexity, segmentation, and image size.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Directional Discrete Cosine Transform for Handwritten Script Identification

Hangarge

Santosh

Pardeshi

2013

2013 12th International Conference on Document Analysis and Recognition

View full text Add to dashboard Cite

Abstract-This paper presents directional discrete cosine transforms (D-DCT) based word level handwritten script identification. The conventional discrete cosine transform (DCT) emphasizes vertical and horizontal energies of an image and de-emphasizes directional edge information, which of course plays a significant role in shape analysis problem, in particular. Conventional DCT however, is not efficient in characterizing the images where directional edges are dominant. In this paper, we investigate two different methods to capture directional edge information, one by performing 1D-DCT along left and right diagonals of an image, and another by decomposing 2D-DCT coefficients in left and right diagonals. The mean and standard deviations of left and right diagonals of DCT coefficients are computed and are used for the classification of words using linear discriminant analysis (LDA) and K-nearest neighbour (K-NN). We validate the method over 9000 words belonging to six different scripts. The classification of words is performed at biscripts, tri-scripts and multi-scripts scenarios and accomplished the identification accuracies respectively as 96.95%, 96.42% and 85.77% in average.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Directional Discrete Cosine Transform for Handwritten Script Identification

Hangarge

Santosh

Pardeshi

2013

2013 12th International Conference on Document Analysis and Recognition

View full text Add to dashboard Cite

show abstract

“…G. G. Rajput and Anita H. B. extracted the feature by transforming an image from time to frequency (i.e. disparity in luminous or color across the image) domain in [7]. Information of image in the time domain is not prominent as compare to frequency domain information.…”

Section: Related Workmentioning

confidence: 99%

Indian Handwritten Script Identification System Based on Random Forest Tree Ensembles

Ganorkar¹,

Rojatkar²

2019

IJRTE

View full text Add to dashboard Cite

 Abstract: The work proposal addresses to introduce a methodology for Indian unconstrained handwritten script identification by practicing distinct features and classifiers. By utilizing classifiers like RF, SVM, k-NN, and LDA for Indian script identification using statistical, geometric, and structural features. To preserve all the information present on handwritten documents such as historical, medieval, inscription, financial administration, public records, government archives, letters, land councils, various agreements, etc. in digitalize form needs textual document processing system (e.g. OCR). To build a precise and productive multi-script/language textual document processing system must have script identification. For this study use, total 1288 (line wise) samples of ten scripts use in India are collected from different persons of different gender, age, education and region (rural or urban). After successful training and testing, 81.8% and 0.252 accuracies and the OOB error rate are achieved by Random Forest respectively. And 77.8%, 73.5%, and 65.5% accuracy is achieved in SVM, k-NN and LDA classifiers respectively.

show abstract

“…The vector, along with the observation vector from all other training images of the same individual, is used to train the Hidden Markov Model for this individual using Baum -Welch algorithm [6], [13]. One hidden markov model is used for each block of character image to test the accuracy.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model

Saxena¹,

Dhaka²

2015

IJCA

View full text Add to dashboard Cite

Handwritten character recognition plays an important role in the modern world. It can solve more complex problems and make the human's job easier. The present work portrays a novel approach in recognizing handwritten cursive character using Hidden Markov Model (HMM) . The method exploits the HMM formalism to capture the dynamics of input patterns, by applying a Gabor filter to a character image, observation feature vector is obtained, and used to form feature vectors for recognition. The HMM model is proposed to recognize a character image. All the experiments are conducted by using the Matlab tool kit.

show abstract

Handwritten Script Recognition Using DCT, Gabor Filter, and Wavelet Features at Word Level

Cited by 9 publications

References 3 publications

Directional Discrete Cosine Transform for Handwritten Script Identification

Directional Discrete Cosine Transform for Handwritten Script Identification

Indian Handwritten Script Identification System Based on Random Forest Tree Ensembles

Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model

Contact Info

Product

Resources

About