On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features

Narang, Sonika Rani; Jindal, M. K.; Ahuja, Shruti; Kumar, Munish

doi:10.1007/s00500-020-05018-z

Cited by 40 publications

(5 citation statements)

References 15 publications

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“…The training of classifiers for the recognition purpose is followed after the feature extraction process. Smart and intelligent machine learning algorithms such as K-NN [17,23], Decision Tree, SVM [20,27,47] are the commonly used classifiers for character recognition. Apart from these classifiers, architecturally advanced classifiers like deep neural networks such as CNN and RNN [32,33,48,49] are extensively used due to their robustness and accuracy.…”

Section: Review Of Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

Recognition of Devanagari Scene Text Using Autoencoder CNN

Shiravale

Jayadevan²,

Sannakki³

2021

ELCVIA

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character-level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.

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Section: Review Of Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

Recognition of Devanagari Scene Text Using Autoencoder CNN

Shiravale

Jayadevan²,

Sannakki³

2021

ELCVIA

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“…In terms of feature extraction, Narang et al Used SIFT feature and Gabor filter feature to extract a priori feature from Devanagari handwritten text, and used SVM to complete classification, and achieved good results (Narang et al 2020). The traditional convolution feature can obtain the end-to-end data features, but it is relatively easy to ignore the spatial order attribute of the image.…”

Section: Related Workmentioning

confidence: 99%

Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes

et al. 2023

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Handwriting recognition is an important application in the field of pattern recognition. Because handwritten Tai Le has similar characters and the proportion of characters with similar shapes is relatively high, this paper proposes a wavelet deep convolution (WDC) feature combined with an ensemble deep variational sparse Gaussian process (EDVSGP) offline handwritten Tai Le recognition method. A Tai Le recognition sample database is constructed, 2 and 3 levels of wavelet decomposition prior features are extracted, and then, the wavelet a priori feature is transformed into a WDC feature. To avoid the problem of dimensionality, LDA+PCA is used to reduce the dimensionality of the feature. Moreover, to better recognize handwritten Tai Le characters, 6 deep variationally sparse Gaussian process (DVSGP) models are integrated as an EDVSGP classification model. Using the handwritten Tai Le character database, the proposed method is superior to the existing methods, with an accuracy, recall and F1-score of 94.15%, 94.15%, and 94.15%, respectively. The universality of the method is verified on the dataset of handwritten Chinese characters with similar shapes (HCFC2021.4) and Devanagari dataset.

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“…The SIFT algorithm or Gabour filters features were used to train the SVM classifier described in. [25] is a manual character organization system written in the Devanagari script. Using a polynomial SVM classifier and 10 times crossvalidation, according to the model, enable it to attain a total recognition accuracy of about 91.39 percent.…”

Section: B) Svm Based Techniquesmentioning

confidence: 99%

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

Shaik Moinuddin Ahmed

2023

IJRITCC

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The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards.

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On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features

Cited by 40 publications

References 15 publications

Recognition of Devanagari Scene Text Using Autoencoder CNN

Recognition of Devanagari Scene Text Using Autoencoder CNN

Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

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