Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey

Singh, Sukhjinder; Garg, Naresh Kumar; Kumar, Munish

doi:10.1007/s11042-022-13318-9

Cited by 25 publications

(10 citation statements)

References 91 publications

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“…This can be done using methods based on a cepstral analysis. Cepstral analysis combines both time and frequency domains, and cepstral features possess several advantages, including source-filter separation, conciseness, and orthogonality, making them convenient for training machine learning algorithms [20][21][22]. It already proved its effectiveness in feature extraction in another acoustic field, speech-oriented applications.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…LPCC is mainly used in noise elimination [30] or music genre classification [31], or speech recognition [32]. PLPC involves critical band spectral resolution, equal-loudness curve, and intensity loudness power law [20]. These cepstral coefficients are mainly used for animal sound classification [29], emotion identification [30], and speech recognition systems [33].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

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Cepstral coefficients effectiveness for gunshot classifying

Svatos,

Holub

2024

Meas. Sci. Technol.

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This paper analyses the efficiency of various frequency cepstral coefficients (fCC) in a non-speech application, specifically in classifying acoustic impulse events - gunshots. There are various methods for such event identification available. The majority of these methods are based on time or frequency domain algorithms. However, both of these domains have their limitations and disadvantages. In this article, an fCC, combining the advantages of both frequency and time domains, is presented and analyzed. These originally speech features showed potential not only in speech-related applications but also in other acoustic applications. The comparison of the classification efficiency based on features obtained using four different fCC, namely Mel-frequency Cepstral Coefficients (MFCC), Inverse Mel-frequency Cepstral Coefficients (IMFCC), Linear-frequency Cepstral Coefficients (LFCC), and Gammatone-frequency Cepstral Coefficients (GTCC) is presented. An optimal frame length for an fCC calculation is also explored. Various gunshots from short guns and rifle guns of different calibers and multiple acoustic impulse events, similar to the gunshots, to represent false alarms are used. More than six hundred acoustic events records have been acquired and used for training and validation of two designed classifiers, Support Vector Machine, and Neural Network. Accuracy, Recall and Matthew's correlation coefficient measure the classification success rate. The results reveal the superiority of GFCC to other analyzed methods.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Section: Feature Extraction Methodsmentioning

confidence: 99%

Cepstral coefficients effectiveness for gunshot classifying

Svatos,

Holub

2024

Meas. Sci. Technol.

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“…This represents the extraction of lexical features such as ngrams and transforming them into a feature set that is usable by a machine learning classifier. It plays a crucial role in text classification and directly influences the text classification model [26]. Term Frequency-Inverse Document Frequency (TF-IDF) is a popular feature extraction method commonly used in text classification and sentiment analysis.…”

Section: Feature Extractionmentioning

confidence: 99%

Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques

Akpatsa¹,

Li²,

Lei³

et al. 2022

IJCAI

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The quest to create a vaccine for covid-19 has rekindled hope for most people worldwide, with the anticipation that a vaccine breakthrough would be one step closer to the end of the deadly Covid-19. The pandemic has had a bearing on the use of Twitter as a communication medium to reach a wider audience. This study examines Covid-19 vaccine-related discussions, concerns, and Twitter-emerged sentiments about the Covid-19 vaccine rollout program. Natural Language Processing (NLP) techniques were applied to analyze Covid-19 vaccine-related tweets. Our analysis identified popular n-grams and salient themes such as "vaccine health information," "vaccine distribution and administration," "vaccine doses required for immunity," and "vaccine availability." We apply machine learning algorithms and evaluate their performance using the standard metrics, namely accuracy, precision, recall, and f1-score. Support Vector Machine (SVM) classifier proves to be the best fit on the dataset with 84.32% accuracy. The research demonstrates how Twitter data and machine learning methods can study the evolving public discussions and sentiments concerning the Covid-19 vaccine rollout program. Povzetek: Narejena je analiza čivkov na Twitterju glede cepiv za

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“…According to a study conducted by Singh et al [3], artificial intelligence is the hottest topic that is used by researchers in handwriting recognition. Despite significant advancements in recognition techniques over the past three decades, handwritten materials have remained predominantly analogue.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

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Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey

Cited by 25 publications

References 91 publications

Cepstral coefficients effectiveness for gunshot classifying

Cepstral coefficients effectiveness for gunshot classifying

Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques

Handwritten Recognition Techniques: A Comprehensive Review

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