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

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

doi:10.1007/978-3-642-25507-6_4

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The consequences of the algorithms are in the arrangement of lines/words/characters (see Figs. 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18). The PPTRPRT technique starts with Preprocessing algorithm by extracting text regions and segmenting the text lines from bilingual offline handwritten script (see Figs.…”

Section: Experiments (S) and Results (S)mentioning

confidence: 99%

“…Natarajan et al [7] proposed a script-independent framework for the multilingual (English, Chinese and Arabic) offline handwriting recognition (OHR) which is based on the hidden Markov model (HMM). Rajput and Anita [8] have presented a multiple-feature-based approach to recognize the script type of the collection of handwritten documents, and eight popular Indian scripts are considered. The wavelets of Daubechies family, discrete cosine transform and Gabor filter are used to extract features and obtained 100 % recognition accuracy for line-level biscripts.…”

Section: Related Workmentioning

confidence: 99%

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Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Sharma

Dhaka

2015

Neural Comput & Applic

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In the Indian subcontinent, a number of languages are in use, and an automatic recognition of printed and handwritten scripts facilitates number of applications such as image document sorting and penetrating online libraries of image documents. This framework proposed a bilingual (English and Hindi) character-spotting framework based on feedforward neural network which works on corpus of bilingual handwritten offline documents. The proposed Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) framework traced the actual text region of the offline handwritten bilingual scripts and lead the process of line segmentation along with skew and de-skew operations. The findings of the iterations were adopted in pixel-spacebased word segmentation, which were further used in character segmentation. Moreover, PPTRPRT performs normalization operation to incorporate all pen-breadth deviations and inscription slant. The proposed framework was state of the art, reflected clearly from the findings of the framework. The proposed framework is proficient to character segmentation and provides accuracy up to 99.78 %.

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Section: Experiments (S) and Results (S)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Sharma

Dhaka

2015

Neural Comput & Applic

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“…Benjelil, Kanoun, Mullot, and Alimi () have used steerable pyramid transforms for script identification at word‐level from printed and handwritten samples in Arabic and Latin scripts. Rajput and Anita () identify scripts at block‐level using a technique involving discrete cosine transform and wavelets of Daubechies family for feature extraction. The k ‐nearest neighbour ( k ‐NN) classifier is used to identify the scripts.…”

Section: Related Workmentioning

confidence: 99%

A clustering‐based feature selection framework for handwritten Indic script classification

et al. 2019

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In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.

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“…In [7][8], the authors proposed a method based on steerable pyramid decomposition to identify Latin and Arabic scripts in handwritten and machine printed types. Some works on handwritten script identification at block and word level are outlined in [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Performance of curvelets, dual-tree complex wavelet and discrete wavelet transform in handwritten word classification

Benjlaiel

Mullot

2014

2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)

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Handwritten words classification is a difficult task due to the high variability and uncertainty of human writing styles. The aim of this work is to Performance of curvelets, dual-tree complex wavelet and discrete wavelet transform in handwritten words classification. Curvelet transform, Dual-Tree complex wavelet transform (DTCWT), Haar, Daubechies, Coiflets, Symlet, Discrete Meyr, Biothogonal and reverse Biothogonal are used in this investigation. Three to four wavelets are chosen randomly from each wavelet family. A dataset of 534 handwritten Latin (HL) and Arabic (HA) word images out of 1068 (267 of each script) are used for training and the remaining is for testing. Energy and entropy are computed at different decomposition sub-bands. Support vector machines (SVM) and 1-NN are used for classification. An exhaustive experimentation is carried out on word images downloaded from IAM Handwriting Database for Latin words and from IFN/ENIT-database for Arabic handwritten words. The experiments showed that the best identification results are achieved using Curvelet transform followed by DTCWT .

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

Cited by 5 publications

References 5 publications

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

A clustering‐based feature selection framework for handwritten Indic script classification

Performance of curvelets, dual-tree complex wavelet and discrete wavelet transform in handwritten word classification

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