# A Comprehensive Study on Feature Extraction Techniques for Indian Sign Language Recognition System

Shafaque Aziz, Mohammad Amjad

► To cite this version:

Shafaque Aziz, Mohammad Amjad. A Comprehensive Study on Feature Extraction Techniques for Indian Sign Language Recognition System. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.104-125, 10.1007/978-3-031-38296-3_9.hal-05466054

HAL Id: hal-05466054

https://inria.hal.science/hal-05466054v1

Submitted on 19 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
<figure><img src="image_3.png" /></figure>

# A Comprehensive Study on Feature Extraction Techniques for Indian Sign Language Recognition System

Shafaque Aziz(✉) and Mohammad Amjad

Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi 110025, India
shafaque.phd@gmail.com, mamjad@jmi.ac.in

**Abstract.** Indian Sign language (ISL) recognition system is an effective communication tool for hearing and speech-impaired sections of society. It is an emerging field of research to facilitate the interaction of the deaf and mute communities with the people of mainstream society. There are broadly four phases in an automated ISL recognition system, i.e. data capturing, preprocessing, feature extraction, and recognition. The phase of feature extraction is a significant phase that decides the efficiency and effectiveness of the recognition system. The quality of the output generated by the feature extraction phase depends upon the feature extraction techniques that are employed. This paper is a comprehensive study of the feature extraction techniques used for the ISL recognition system. A detailed classification of the currently employed techniques for ISL are introduced. The study also presents the advantages and limitations of each feature extraction technique used in the vision-based ISL recognition system.

**Keywords:** Indian Sign Language (ISL) · Vision based · Feature Extraction · Feature Extraction Techniques · Human Computer Interaction (HCI)

## 1 Introduction

Language is a means of communication needed by humans to better express themselves. The privileged members and the especially abled deaf and mute communities alike need languages for conveying their emotions and day-to-day requirements. Sign language, a means of non-verbal communication, facilitates the deaf and the mute communities of the societies in their communicative interaction. It is best suited for connecting hearing and speech impaired communities with people around the globe. It limits the barriers for deaf and mute communities [1, 2]. Different sign languages are prevalent around the globe covering different demography, e.g. American Sign Language (ASL), Arabian Sign Language (ArSL), British Sign Language (BrSL) etc. Sign languages show resemblance to the local communicative languages of the region [3, 4]. It uses different signs comprising hand gestures, facial expressions, body postures etc. and can be either one handed or two handed. Majority of sign languages engage primarily the upper part of the body [5, 6].
ISL is an example of sign language that facilitates communication for hearing and speech impaired communities especially in India that is estimated to have more than two million people with speech and/or hearing impairment, with about a million adults and half a million children among them using ISL [7]. ISL is considered a complete language with associated linguistic attributes like grammar, syntax, vocabulary that are specific to ISL [8]. Alphabets, words and sentences can be communicated in ISL via both one handed gesture and two handed gestures. ISL is different from other contemporaries' sign languages like ASL, BrSL in certain ways. One of the most noticeable variations is its lexicon that shows significant level of iconicity. The other variation that separates ISL from most of the sign languages is the presence of a large number of compound signs in ISL. Use of compound signs is pervasive in ISL, while it is very restricted in other sign languages [46].

In practice, the hearing community is not very adept in using ISL. Its understanding and practical implementation is very complex and troublesome for non-signers. Consequently, trained sign language interpreters become indispensable for situations like medical consultations, legal engagements, banking assignments etc. Thus, the deaf and the dumb communities are primarily dependent on the interpreters to communicate with the privileged members of the society and vice-versa. Moreover, India houses only about 300 licensed interpreters who know and understand the ISL. This causes major hindrance in communicative interaction between deaf and mute communities and rest of the society [9, 10]. Thus an ISL recognition system is needed to facilitate sustenance of an inclusive society wherein people with hearing and speech impairment get equal opportunities for their growth and a dignified life.

Sign Language Recognition (SLR) is a collaborative research area that has amalgamated different domains including pattern matching, computer vision, natural language processing (NLP) etc. It facilitates the classifications of different signs of a sign language by proposing and employing different methods and algorithms. This helps to actualize a system that further facilitates the understanding of a sign language. SLR depends on different factors, e.g. grammar of sign language, syntax, vocabulary, etc. Additionally, the meaning corresponding to any hand gesture, posture or expression that are included in any sign language, also determines the subsequent translation and recognition of sign language. This further facilitates the generation of text corresponding to the signs of the deaf and the mute people. This allows privileged communities of the society to better interact with the deaf and the mute communities [11, 13].

The primary task of SLR is to take an input pattern and correctly assign it as one of the possible output classes. An automated SLR system broadly incorporates four phases in its operation, e.g. capturing images and/or videos, pre-processing, feature extraction, and recognition (Fig. 1).

There are different categorizations of SLR system. Based on the capturing methods, SLR systems are broadly categorized into two categories, as shown in Fig. 2. Sensor based SLR system and Vision based SLR. In Sensor based SLR systems, sensor enabled devices e.g. Electromyography (EMG), Inertial measurement unit (IMU), sensor and coloured data gloves etc. are used to capture the data. The Vision based SLR systems use camera/web camera, kinect sensors and leap motion controller (LMC) for data acquisition [25].
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Phases in Automated SLR System. (* src: http://www.deaftravel.co.uk/signprint.php?id=27)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** SLR System based on Capturing Methods</figcaption></figure>

Vision based SLR systems show significant benefits over sensor based SLR systems, e.g. parameters like colour and texture can easily be incorporated during the recognition through vision based systems as compared to sensor based SLR systems [11]. The captured data is preprocessed to get the images or video (the sequence of images). The images or the videos are further passed through the phase of feature extraction, which is considered the most crucial phase in the SLR system. The phase of feature extraction helps in actualising gesture classification and derives suitable information that categorizes each class [12].

In this research, we have made an intensive study on different feature extraction techniques that have been implemented for the recognition of vision-based ISL. The research proceeds with brief description about features in ISL in Sect. 2. In Sect. 3, feature extraction and different types of feature extraction techniques that have been used in vision-based ISL recognition system are summarized. Section 4 discusses the current state of literature and studies related to feature extraction techniques of ISL recognition system. In Sect. 5 we have analysed the findings of our study. Section 6 includes conclusion summarising the findings of our research and scope for future enhancements.

## 2 Features in ISL

SLR handles the physical dynamics and movements of hands, facial expressions, finger movements, body postures etc. [14]. Thus, features have very significant roles in the SLR systems. They determine the characteristic behavior of a sign e.g. the size, storage taken, ease in classification, time taken to classify etc. In other words, features illustrate an image or an object in an efficient manner [17]. Superior and adequate features are
indispensable for effective classification and recognition [18]. These gestures associated with sign language vary significantly and incorporate large set of variations. The features associated with the gestures must be effective and reliable enough to handle the complexities of such variations. The extent to which the SLR system handles these complexities determines its effectiveness and practicality [8].

As sign languages are non-verbal in nature, communication is primarily visual, combining manual features and non-manual features of expression [19], as shown in Fig. 3. Manual features (MF) include gestures that incorporate the shape and motion of the hands to deliver message. The non-manual features (NMF) incorporate facial expressions and body postures. These features either resemble a sign or help improve the understanding of the sign from the manual gestures [15, 16]. In ISL, most of the signs, for its representation either make use of both the hands termed two-handed signs or are multi-modal, using the combination of manual gestures such as hand movement and non-manual gestures like facial expressions, gaze, lip movement etc. [20]. Additionally, ISL comprises of compounding signs as well, thus making it a language of considerably large lexicons. For example, to define sign for sister, gestures showing female and siblings are compounded [21].

<figure><img src="image_6.png" /><figcaption>Fig. 3. Features in Indian Sign Language</figcaption></figure>

## 2.1 Manual Features

Manual features include the dynamics of the hands, e.g. hand shape, hand location and movement, orientation of palm, trajectory of the hand motion etc. to convey the message in sign language as shown in Fig. 4. These hand dynamics are the underlying parameters for ISL [19]. These parameters are used differently for different sign languages. The finger-tips, direction of the figures, hand contours etc. together determines the hand shape. For example if the fist is open or closed [22]. Hand location and orientation in a sign language refer to position of hand e.g. on lips and direction of palm and pointing
fingers respectively. Hand Movements are the action performed by hands and trajectory shows the path of this movement [23].

Manual features can also be recognized using continuous or isolated hand configurations [24, 25]. The hand shape and the hand movements are factors deciding the meaning of the words or sentences corresponding to the manual gestures. There are certain attributes e.g. point of interest (POI) of the hands that decide state of hand gestures. For example we can consider two POI corresponding to the ‘shape’ and ‘direction of movement’ that will help in the recognition of manual features in a sign language [26].

<figure><img src="image_7.png" /><figcaption>Fig. 4. Manual features showing ISL alphabets [30]</figcaption></figure>

## 2.2 Non-manual Features

There are certain gestures e.g. facial expressions, head, mouth and eye movement, eyebrow motion, and body position etc. that constitute an integral part of the sign language. These are called non-manual features of the sign language [15, 16] as shown in Fig. 5. These non-manual features help in the understanding of the manual gestures. The manual and the non-annual features are combined to get better expression of the signs. In some cases, non-manual features compliment the manual features for better understanding and recognition of sign language e.g. by handling the ambiguities in the meaning of manual gestures [24, 27]. In other cases the non-manual features may have an intrinsic meaning especially to represent negations that may alter the meaning of the signs corresponding to the manual gestures [22]. For example lowering and raising of the eyebrows
help in conveying the grammatical information and better expressing emotions [19]. Eye gaze, lip movements and usage of tongue help to show affirmation or negation. Apart from facial features, movement of shoulders, neck and head help add clarity to the hand gestures [19, 28].

<figure><img src="image_8.png" /><figcaption>Fig. 5. Some frames showing Facial expressions for ISLR [53]</figcaption></figure>

## 3 Feature Extraction

Feature extraction is the phase that follows the preprocessing phase of the automated SLR system. It is considered one of the most crucial phases in the SLR system. In this phase, different features e.g. shape based descriptors, histogram of oriented gradient, scale invariant feature etc. are extracted. The output generated out of here is crucial for different classifiers and for SLR in general as the inclusion of irrelevant features leads to incorrect classification and consequently inefficient sign recognition [29].

Feature extraction simplifies the task of sign classification through formal procedure by describing appropriate information embedded in a pattern. It retrieves the most relevant details from the elementary data and defines parameters which help in elucidating the meaning corresponding to a given sign effectively and accurately [12, 14]. In other words, feature extraction provides mechanism for dimensionality reduction in diverse applications of image processing and pattern recognition. It helps in listing the most informative and favorable features that help in improving the precision of the data to be tested, thus boosting the accuracy and the speed of SLR [31, 32].

The phase of feature extraction includes feature selection that generates feature vectors as shown in Fig. 6. Feature vectors simplify the learning of any neural model to gauge the association of features with specific classes [33].
<figure><img src="image_9.png" /><figcaption>Fig. 6. Stages of Feature Extraction process</figcaption></figure>

Thus the process of feature extraction put forward a large set of data in a precise and simplified manner. Several feature engineering methods and tools are employed for extracting the features. The final set of features and corresponding classifications are finally optimized to upscale the accuracy, adaptability and credibility of the SLR system [33, 34].

## 3.1 Feature Extraction Techniques

The phase of feature extraction generates large set of features in a precise and simplified manner. To perform this task in an efficient and effective manner, there are different techniques that are employed in ISL. These are termed the feature extraction techniques. These techniques are indispensable for the success of the feature extraction phase of ISL recognition. As the situation demands, practitioners have employed varying techniques. In our research we have made a comprehensive list of the techniques that have been used in the literature related to ISL recognition. We have divided the techniques into various categories as shown in Table 1. Each category contains a list of feature extraction techniques.

**Eigen Vector and Eigen Value based Techniques.** These techniques are part of linear transformations. Eigen vectors give direction along which linear transformations work while Eigen values determine the factor through which the linear transformations occur [48].

*Principal Component Analysis (PCA)*: It is considered one of the most successful and one of the oldest techniques for the purpose of feature extraction and data representation. PCA, termed Karhunen-Loeve expansion [35], is an algebraic method employing the concepts of matrix. It performs dimensionality reduction on the matrix such that only essential details remain in the matrix for processing. PCA finds its application in pattern recognition and computer vision e.g. face recognition etc. [36, 37].

The PCA can be calculated as follows:

1. Calculate mean vector '$\mu$' of the d-dimensional dataset as shown in Eq. 1

$$ \mu = 1/n \sum_{i=1}^{n} x_i \qquad (1) $$

2. Calculate covariance matrix, $M_C$, by the Eq. 2.

$$ M_C = \sum_{i=1}^{n} (x_i - \mu) \cdot (x_i - \mu)^C \qquad (2) $$
3. Compute Eigen vectors and corresponding values.

4. Eigen vectors are sorted in decreasing order of Eigen values.

The Eigenvectors with lower Eigen values have relatively fewer details for data distribution, and thus are filtered-out to minimize the dimensionality of data [25].

**Illumination invariance based techniques.** It is an extension of homomorphic filtering that uses logarithmic point transformation. Here the illumination invariance function is constant on sign images observed for different point of illuminance [40].

*Histogram of Orientation Gradient (HOG):* The individual cells of the image have histogram that is created using directions and the magnitudes of pixel gradients for the corresponding cell. The process is repeated over a block of pixels and are then normalized to actualise the features [40]. HOG measures the orientation of local intensity gradients describing the articulated or occluded gestures in ISL. The articulated gesture is a problem particularly in two hand gestures of ISL. HOG tries to solve this problem but the efficiency is very poor. But HOG descriptors are very stable against any variations in illumination and orientation [38, 39].

<table><caption>**Table 1.** Various categories of feature extraction techniques for ISL recognition</caption><thead><tr><td>Categories</td><td>Feature Extraction Technique</td><td>Studies</td></tr></thead><tbody><tr><td>Eigen vector and Eigen value based</td><td>PCA</td><td>[37, 48, 54, 56, 65]</td></tr><tr><td>Illumination invariance based</td><td>HOG, HOEF, SIFT, SURF</td><td>[39, 57, 64, 68]</td></tr><tr><td>Frequency domain based</td><td>Fourier Descriptor, Wavelet transformation, B-Spline</td><td>[43, 45, 53, 58, 59, 62, 66]</td></tr><tr><td>Moments transformations</td><td>Zernike Moments, Hu's Moments, Euclidean distance</td><td>[55, 60, 69, 70]</td></tr><tr><td>Soft computing based</td><td>NN, Fuzzy Logic</td><td>[7, 21, 67, 71–73]</td></tr></tbody></table>

*Histogram of edge frequency (HOEF):*

This technique identifies the edge of an image and takes account of distribution of the edge pixels in the localized segment of the image. This technique makes sure that the localized details of the image is preserved. This technique solves the problems of HOG technique and generates comparatively better results for the problem of occlusion in ISL [39].

*Scale and Rotation Invariant Feature Extraction Techniques (SIFT):* SIFT was proposed by Lowe [41]. In SIFT, an image is described by its interest points (IP). Every sign image may have different number of IPs. SIFT extracts distinctive IPs from sign images that can facilitate matching between different views of a sign image. The SIFT is robust against any variations in scaling, rotation, translation, illumination, orientation etc. and generates a vast collection of local feature vectors for the sign image. The scaling and
the smoothening of the sign image are performed by Gaussian function (difference of Gaussians (DoG) operation) [14, 25, 42].

**Speeded up Robust Features (SURF):** It is a feature extraction technique whose development is based upon SIFT but is relatively faster in extracting features than SIFT. But SURF requires setup wherein the capturing device like camera should be stationed in a horizontal position for better performance. Additionally, SURF is illumination dependent that works as a disadvantage [19]. SURF employs an *integer approximation of the determinant of Hessian blob detector* to obtain IPs. Integral image is the summation of the intensity values for the IPs of the sign image [25].

**Frequency Domain Based Techniques.** Feature extraction techniques based on frequency domain transform the time domain input into frequency domain [25].

**Fourier Descriptor:** Fast-Fourier transform of the sign image gives the Fourier descriptor of image. Fourier descriptors are very effective in shape recognition since they present invariance in scaling, rotation and translation [43]. This technique encodes the shape of a 2D sign image wherein every IP on the edges maps to a complex number. It also generates even and simple boundaries by employing the inverse transformation that may even facilitate recovering the original shape [12].

**B-Splines:** These are piecewise polynomial functions that generates localize destinations to shape/surfaces by utilizing limited number of IPs. It is a generalized form of Bezier curve and is a very superior alternative for representing curves due to its spatial uniqueness, coherence and boundedness, localized shape management and robustness against affine transformation [45].

**Wavelet Transform:** It is a series expansion methodology that facilitates the representation of signals at varying intensity of resolution [17]. It is a favorable technique for multi-resolution sign image decomposition employing wavelet coefficient and scaling making it consistent and unvarying to orientation [18].

**Principle Curvature Based Region (PCBR) Detector:** It detects semi-localized structural cues e.g. boundaries and curvilinear shapes which show robustness to the changes in intensity, coloration, orientation etc. PCBR also shows high accuracy in recognizing objects and identifying watershed regions in an image [47].

**Moments Transformations techniques.** The techniques based on the moments' transformation provide a very compact characterization of a sign image and robustness to noise on account of its orthogonality feature. Moments of a sign image are the coefficients of the Moment Transform [44].

**Zernike Moments:** The feature extraction with Zernike Moments has better precision for hand gesture recognition systems [49]. The magnitude part of Zernike moments is unvarying to rotation while the phase is variant to rotations. Zernike moments handle discretization errors and remove high frequency noise. This is significantly important for the recognition of ISL [60].

**HU'S moments:** Extracting features with Hu's moment looks for the non-localized features and invariant moments of an image. The invariants of an image are statistical
measures that stay constant against any changes due to transformations, like rotation, scaling and translation. Such measures function promptly with area of pixels. [52].

**Euclidean Distance (ED):** Mathematically, ED calculates the rectilinear distance between a pair of pixel points of a sign image with linear transformation. It is one of the most common used method but incorporates time-consuming operations e.g. square root, floating point operations etc. The ED transformation is invariant to the rotatory motion of the sign image [70].

Euclidean distance can be evaluated using Eq. 3:

$$ED(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2} \qquad (3)$$

**Soft computing based techniques.** Soft computing shows competence in solving real-life problem e.g. SLR. It can handle the ambiguity, imprecision, vagueness etc. associated with the sign language, thus achieving robustness and decision making capabilities similar to human [50].

**Neural Network (NN):** It includes two important networks i.e. artificial neural network (ANN) and Convolutional Neural Network (CNN) for the purpose of feature extraction in ISL. Artificial neural network (ANN) finds its application in numerous domains. It is a non-linear network with high degree of fault tolerance. ANN very well handles imprecision and information. It focusses on the edges and facilitates the identification of different features that show invariability to translatory motion, rotatory movements, shear, scale, position, illumination, stretch etc. CNNs are favorable for extracting natural features and performs better on computer vision data [12].

In neural network, 'a_n' can be the chosen input having weight 'W_n' associated with it, generating some net input.

Here, 'n' is the input layer.

The total\_calc input for threshold value is calculated using Eq. 4, [51]:

$$total\_calc_n = \sum_{i}^{n} a_n W_n \qquad (4)$$

**Fuzzy Logic:** Fuzzy rules create relation between inputs and desired outputs. It handles vague and ambiguous data as may be the case with the sign images and facilitates the handling of the uncertainty in the data [67].

**Hybrid Feature extraction techniques:** It is a union of multiple feature extraction techniques to boost the effectiveness of ISL recognition system.

## 4 Related Work

Automation of sign languages used across the world has seen significant rise in recent times. Scientists and researchers are pitching their ideas and efforts to enhance the effectiveness and performance of automated SLR framework. There has been efforts to
reduce the dependency of the hearing and speech impaired community on the manual interpreters. The onset of COVID19 has given a different realm to this problem and has projected a prioritized need for researchers to enhance the efficiency and practicality of SLR. The ISL has also seen significant rise in efforts of the researchers in this domain. Efficiency of any SLR framework, and ISL in particular needs effective, efficient and reliable feature extraction phase. This in turn very critically depends on the feature extraction techniques that must be employed to extract different relevant manual and the non-manual features corresponding to ISL. Researchers have been effectively implementing different feature extraction techniques that may help in increasing the reliability and the efficiency of the ISL recognition framework.

Mali et al. have proposed ISL recognition employing SVM classifiers [37]. The authors have used isolated sign images corresponding to the hand gestures in ISL and have implemented feature extraction algorithm built upon PCA. This algorithmic technique lowers the count of features and helps in displaying the data in a reduced dimensionality space.

Sawant and Kumbhar [56] have put forward a real-time gesture recognition system in ISL that is divided in four modules. The authors have used PCA technique that extracts relevant features of the ISL gestures. The recognized gestures are then transformed into text and audio format.

Similarly a multi-modal recognition framework for sign language has been proposed by Kumar et al. [65], where the authors have incorporated sign gestures with the facial expressions to portray the emotional state of signer. Hidden Markov Model (HMM) and Independent Bayesian Classification Combination (IBCC) are introduced to facilitate recognition.

Das et al. [57] have also proposed a real-time system for the classification of facial gestures by extracting the lip pattern through texture and shape. The HOG of the lip image constitute the extracted characteristic for recognizing the ISL.

Table 2 shows the studies containing Eigen vector and Eigen value based feature extraction techniques and corresponding extracted features.

There are different illumination invariant feature extraction techniques e.g. HOG, HOEF, SIFT, SURF etc. as shown in Table 3. Lilha and Shivmurthy [39] in their research have evaluated HOG and HOEF for dual-handed sign language alphabets. The authors have concluded that the accuracy of HOEF (98.1%) was found to be better as compared to the accuracy of HOG (71.4%) for the ISL recognition system. Patil and Sinha [64] through their research on ISL have extracted distinct features from ISL gestures by implementing different stages of SIFT. Katoch et al. [68] have presented a technique that utilizes Bag of Visual Words (BOVW) Model for the recognition of ISL alphabets and numbers from a live video. SURF features are extracted for subsequent mapping and classification.

Additionally, there are some frequency domain based feature extraction techniques as shown in Table 4. For example Fourier Descriptors [58, 62, 66], B-Spline [45], Wavelet transform [53, 59] etc., that are employed by researchers to handle the complexities associated with the ISL double hand gestures. Geetha and Manjusha [45] have put forward a novel vision-based ISL recognition framework using B-Spline approximations for alphabets and numbers. Shukla et al. [43] in their work have proposed a novel Fourier
<table><caption>**Table 2.** Eigen vector and Eigen value based feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Features Extracted</td><td>Feature Extraction Technique</td><td>Sample Size</td><td>Classification Technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[48]</td><td>Hand gesture</td><td>Eigen values and Eigen vectors</td><td>240 samples of alphabets</td><td>Euclidean distance</td><td>97.0%</td></tr><tr><td>[54]</td><td>Hand motion and gesture</td><td>Eigen values and Eigen Vectors</td><td>480 videos of alphabets from 20 signers</td><td>Eigen value Weighted Euclidean distance</td><td>96.25%</td></tr><tr><td>[65]</td><td>Fingertip positions, palm center position + face</td><td>PCA, Deformable Model Fitting</td><td>51 dynamic sign word gestures</td><td>HMM</td><td>96.05%</td></tr><tr><td>[37]</td><td>Hand gesture</td><td>PCA</td><td>192 images of alphabets</td><td>SVM</td><td>95.31%</td></tr><tr><td>[56]</td><td>Hand motion and gesture</td><td>PCA</td><td>260 samples of alphabets</td><td>Euclidean distance</td><td>---</td></tr></tbody></table>

<table><caption>**Table 3.** Illumination invariance based feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Feature Extracted</td><td>Feature Extraction Technique</td><td>Sample Size</td><td>Classification Technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[39]</td><td>Palm shape</td><td>HOG, HOEF</td><td>780 samples from 3 signers</td><td>SVM</td><td>74.0%, 98.10%</td></tr><tr><td>[57]</td><td>Face (lip image)</td><td>HOG</td><td>296 samples of 4 alphabets A,B,C,D</td><td>ANN</td><td>90.67%</td></tr><tr><td>[64]</td><td>Hand gesture</td><td>SIFT</td><td>260 samples of alphabets from 10 signers</td><td>--</td><td>--</td></tr><tr><td>[68]</td><td>Hand shape</td><td>SURF</td><td>108 samples of alphabets and numeric signs from 3 signers</td><td>SVM, CNN</td><td>99.01%, 99.05%</td></tr></tbody></table>

descriptors based approach for extracting hand shapes in ISL. For recognizing gestures for the English alphabets, *Dynamic Time Warping* has been used.

Researchers have also implemented feature extraction techniques for ISL based on moments transformations, e.g. Zernike Moments [55, 60], Hu's moments [69], Euclidean Distance [70] etc. as shown in Table 5. For example Sharma et al. [60] in their research have analyzed the behavior of Zernike Moments containing two parameters i.e. magnitude and orientation. The authors have examined the potential of Zernike Moments in
<table><caption>**Table 4.** Frequency domain based feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Feature Extracted</td><td>Feature Extraction technique</td><td>Sample Size</td><td>Classification Technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[45]</td><td>Shape, texture and finger features of each hand</td><td>B-Spline curve</td><td>Static Alphabets and Numerals</td><td>SVM</td><td>90.0%</td></tr><tr><td>[53]</td><td>Face</td><td>Wavelet transform</td><td>100 videos of 26 different alphabets (A-Z) from 10 signers</td><td>SVM</td><td>80.0%</td></tr><tr><td>[58]</td><td>Shape feature</td><td>Fourier descriptors</td><td>144 samples of 26 different alphabets (A-Z) and 10 numeric signs (0-9)</td><td>ANN, Back propagation algorithm</td><td>95.10%</td></tr><tr><td>[43]</td><td>Hand Shape</td><td>Fourier Descriptors</td><td>338 samples of 26 different alphabets (A-Z)</td><td>KNN</td><td>96.15%</td></tr><tr><td>[59]</td><td>Hand Shape</td><td>Wavelet transform</td><td>100 videos of ISL sentences from 5 different signers</td><td>HMM</td><td>90.0%</td></tr><tr><td>[62]</td><td>Shapes of hands and head</td><td>Fourier descriptors</td><td>335 samples of 26 different alphabets (A-Z)</td><td>Back propagation algorithm</td><td>92.34%</td></tr><tr><td>[66]</td><td>Hand gesture and tracking</td><td>Fourier descriptor</td><td>72,000 videos of numbers, alphabets and phrases</td><td>Euclidean distance</td><td>97.5%</td></tr></tbody></table>

recognizing the shape and the orientation of ISL gestures. In another study, Adithya et al. [70] have proposed a system that recognizes the signs of ISL determined by features, extracted from hand shapes. Segmentation technique based on the skin tone extracts the hand segment from the sign.

More recently, many soft computing based feature extraction techniques e.g. Neural Network [21, 72, 73], Fuzzy Logic [67] etc. have found prominence in ISL recognition as shown in Table 6. Gangrade and Bharti [71] in their research have proposed an algorithm to point and extract hand region from sign image, applying Microsoft Kinect sensor. CNN are then employed to obtain features from ISL signs. The research has shown promising result for complex environment involving skin color background, hands overlapping the facial gestures etc.
<table><caption>**Table 5.** Moments transformations based feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Features Extracted</td><td>Feature Extraction Technique</td><td>Sample Size</td><td>Classification technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[70]</td><td>Hand shape</td><td>Euclidean Distance</td><td>360 samples of alphabets and numerals</td><td>ANN</td><td>91.11%</td></tr><tr><td>[55]</td><td>Hand shape</td><td>Zernike Moments</td><td>318 samples of 19 alphabets</td><td>Euclidean Distance</td><td>97.22%</td></tr><tr><td>[60]</td><td>Hand Shape and Orientation Recognition</td><td>Zernike Moments</td><td>720 samples of five alphabets (C, I, L, T, V)</td><td>Naïve Bayes (NB), Multilayer Perceptron</td><td>94.40%</td></tr><tr><td>[69]</td><td>Hand gestures</td><td>Hu's Moment</td><td>Samples of V,O in Hindi/English</td><td>KNN</td><td>84.40%</td></tr></tbody></table>

<table><caption>**Table 6.** Soft computing based feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Features Extracted</td><td>Feature Extraction technique</td><td>Sample Size</td><td>Classification Technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[73]</td><td>Hand shape palm and finger</td><td>Customized CNN</td><td>140 samples of alphabets (A-Z) and numeric signs (0-9) from 15 signers</td><td>Softmax</td><td>99.0%</td></tr><tr><td>[21]</td><td>Hand gesture, arms and shoulder</td><td>MobileNetV2, DenseNet201, ResNet50v2 and Inceptionv3 &amp; biLSTM</td><td>4,287 videos and over 263 word signs</td><td>Xgboost</td><td>92.10%</td></tr><tr><td>[7]</td><td>Hand movement</td><td>9 layer CNN</td><td>5157 samples of alphabets</td><td>Softmax</td><td>98.64%</td></tr><tr><td>[67]</td><td>Face and hand</td><td>Fuzzy Membership functions</td><td>382 videos of sentence</td><td>K-means KNN</td><td>81.0%</td></tr><tr><td>[71]</td><td>Hand detection</td><td>CNN</td><td>43200 samples of alphabets and numerals</td><td>Softmax</td><td>99.30%</td></tr><tr><td>[72]</td><td>Hand gesture</td><td>CNN</td><td>35,000 samples of different words</td><td>Softmax</td><td>99.72%</td></tr></tbody></table>
Researchers have also explored the combination of different feature extraction techniques for ISL [14, 47, 61, 63], termed the hybrid feature extraction techniques as shown in Table 7. For example Tripathi and Nandi [63] have worked on the recognition of continuous gestures. The authors have used *Gradient based key-frame* extraction technique by dividing continuous gestures into sequential signs and deleting irrelevant frames. The features are extracted and dimensionally reduced by using Orientation Histogram in synchronization with the PCA. The results obtained were very promising.

<table><caption>**Table 7.** Hybrid feature extraction techniques for ISL recognition</caption><thead><tr><td>Author</td><td>Features Extracted</td><td>Feature Extraction technique</td><td>Sample Size</td><td>Classification technique</td><td>Accuracy</td></tr></thead><tbody><tr><td>[14]</td><td>Hand shape and orientation</td><td>HOG and SIFT</td><td>235 samples of 36 different gestures by 4 signers</td><td>MSVM</td><td>93.0%</td></tr><tr><td>[63]</td><td>Hand shape</td><td>Orientation histogram and PCA</td><td>100 video samples by 5 signers</td><td>Euclidean distance, Cosine distance, Correlation distance</td><td>92.80%, 86.0%, 91.0%</td></tr><tr><td>[61]</td><td>Hierarchical Centroid and Euclidean Distance</td><td>Face region and Hand tracking</td><td>24 samples of videos</td><td>Dynamic Time Warping</td><td>91.0%</td></tr><tr><td>[47]</td><td>Shape, texture and finger features of each hand</td><td>PCBR detector, Wavelet Packet Decomposition (WPD-2) and complexity defects algorithms</td><td>230 images and 60 dynamic videos of alphabets</td><td>Multi class non-linear SVM</td><td>91.30%, 86.30%</td></tr></tbody></table>

# 5 Discussions

Through our study we have summarised different feature extraction techniques that are being employed during ISL recognition. We have mainly considered the studies from 2011 to 2022 and have found five broad categories of the feature extraction techniques as summarised in Table 1. Each of these techniques have their own advantages and limitations as summarised in Table 8.

The Eigen vector and Eigen value based techniques e.g. PCA is one of the oldest technique and that reduces computational complexities but fails to be efficient in identifying false gestures. The HOG technique, although is invariant to illumination but shows
<table><caption>**Table 8.** Advantages and Limitations of feature extraction techniques</caption><thead><tr><td>Feature Extraction Technique</td><td>Advantages</td><td>Limitations</td></tr></thead><tbody><tr><td>PCA</td><td>Less sensitive to noise<br>Reduces computational<br>complexity</td><td>Not good for identifying false<br>gestures</td></tr><tr><td>HOG</td><td>Invariant to illumination and<br>preprocessing of image</td><td>Low accuracy for two hands<br>ISL because of occlusion and<br>ambiguity<br>Variance to scaling and<br>rotation</td></tr><tr><td>HOEF</td><td>Gives better accuracy for two<br>hand ISL signs and solves<br>occlusion and ambiguity</td><td>High sensitivity to rotation</td></tr><tr><td>SIFT</td><td>Invariant to scaling and<br>rotation<br>Illumination independent</td><td>Speed is slow</td></tr><tr><td>SURF</td><td>Faster processing speed</td><td>Lack of rotational invariance<br>Illumination dependent</td></tr><tr><td>Fourier<br>Descriptor</td><td>Invariant to size and rotation<br>Gives smooth boundaries</td><td>Bad noise resistance and<br>affine transformations</td></tr><tr><td>Wavelet<br>transform</td><td>Gives information of both<br>frequency and location<br>Invariant to orientation</td><td>Bad contour properties if<br>occlusion is present</td></tr><tr><td>B-Spline</td><td>Smooth and continuous result<br>Invariant to shape</td><td>Bad contour properties if<br>occlusion is present</td></tr><tr><td>Zernike<br>Moments</td><td>Rotational invariant</td><td>Orientation and illumination<br>of background needs to be<br>managed<br>High complexity</td></tr><tr><td>Hu's Moments</td><td>Stable for geometric features<br>Not affected by scaling,<br>translation, orientation and<br>reflection<br>Robust for infinite resolution<br>based image</td><td>Orientation and illumination<br>of background needs to be<br>managed<br>High complexity</td></tr><tr><td>Euclidean<br>distance</td><td>Rotational invariant</td><td>Time taking calculations</td></tr><tr><td>NN</td><td>Invariant to all<br>transformations, illumination<br>and stretch</td><td>Requires large computational<br>power</td></tr></tbody></table>

(continued)
<table><caption>**Table 8.** (continued)</caption><thead><tr><td>Feature Extraction Technique</td><td>Advantages</td><td>Limitations</td></tr></thead><tbody><tr><td>Fuzzy Logic</td><td>Segmentation can be substituted<br>It handles smoothening of image and removal of noise</td><td>Complex Computations and calculations</td></tr></tbody></table>

variance against scaling and rotation of the sign images. It also has low accuracy while dealing with two-hand gestures that is prominent in ISL. Some drawbacks of HOG in ISL recognition is improved using HOEF technique that solved the problem of occlusion and ambiguities. It also showed enhanced accuracy for two-handed ISL gestures. The SIFT is invariant to scaling and rotation, but has relatively slower operational speed. The SURF technique possesses faster operational speed but there is lack of rotational invariance. The frequency domain based feature extraction techniques e.g. wavelet transform, B-Spline exhibit bad contour properties during occlusion. The more recent soft computing feature extraction techniques for ISL have shown promising results in accuracy but have relatively higher computational complexities.

Apart from these existing feature extraction techniques, some researchers have collaborated different techniques to generate a hybrid model with an aim to increase the efficiency, effectiveness and reliability of the ISL recognition system. Figure 7 depicts the distribution of the research corresponding to the feature extraction techniques for ISL recognition.

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Distribution of feature extraction techniques for ISL recognition</figcaption></figure>

## 6 Conclusions and Future Scope

The ISL recognition is a blessing for the deaf and the mute communities. The efficiency in the task of recognizing the gestures corresponding to ISL depends on various phases of ISL recognition. Feature extraction is a very important phase of SLR, thus an efficient feature extraction phase contribute positively in the ISL recognition framework. An
efficient feature extraction phase depends upon feature extraction techniques that are employed for different tasks of feature extractions. From the literature many feature extraction techniques are identified based on hand and palm gestures, hand shapes, facial expressions, hand movements, body postures etc.

From the literature we could find that majority of the feature extraction techniques for ISL have focused on manual features. The feature extraction techniques for non-manual features can still be explored significantly. For ISL, majority of the feature extraction techniques have been confined to static and isolated gestures, recognizing only characters, numerals and some words. The future aspect can include feature extraction techniques for continuous gestures related to sentences and phrases.

Moreover, the set of frequency domain based feature extraction techniques have been used extensively in the literature. PCA technique is the most commonly used feature extraction technique among Eigen vector and Eigen value based techniques. In recent times, soft computing based techniques and hybrid techniques have made significant contributions in the ISL recognition framework.

Significant research is underway in the area but throughout the literature we could not find any large public dataset of ISL that can be chosen as standard for comparative and exploratory research.

## References

1. Tomkins, W.: Indian sign language, vol. 92. Courier Corporation (1969)

2. Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12) (1998)

3. Power, J.M.: Historical Linguistics of Sign Languages: Progress and Problems. Frontiers in Psychology 13 (2022)

4. Karpov, A., Kipyatkova, I., Zelezny, M.: Automatic Technologies for Processing Spoken Sign Languages. Procedia Computer Science 81 (2016)

5. Dhanjal, A.S., Singh, W.: An automatic machine translation system for multi-lingual speech to Indian sign language. Multimedia Tools and Applications 81(3) (2022)

6. Sultan, A., Makram, W., Kayed, M., Ali, A.: Sign language identification and recognition: a comparative study. Open Computer Science 12(1), 191–210 (2022)

7. Sruthi, C.J., Lijiya, A.: SigNet: A deep learning based indian sign language recognition system. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019 (2019)

8. Sinha, S.: Indian sign language: An linguistic analysis of its grammar. In: Indian Sign Language: An Linguistic Analysis of Its Grammar (2017)

9. Sood, A., Mishra, A.: AAWAAZ: A communication system for deaf and dumb. 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 620–624 (2016)

10. Sharma, P., Tulsian, D., Verma, C., Sharma, P., Nancy, N.: Translating speech to indian sign language using natural language processing. Future Internet 14(9), 253 (2022)

11. Wadhawan, A., Kumar, P.: Sign language recognition systems: a decade systematic literature review. Archives of Computational Methods in Engineering 28(3), 785–813 (2019). https://doi.org/10.1007/s11831-019-09384-2

12. Tyagi, A., Bansal, S.: Feature extraction technique for vision-based indian sign language recognition system: A review. Adv. Intell. Syst. Comput. 1227, 39–53 (2021)
13. Rastgoo, R., Kiani, K., Escalera, S.: Sign language recognition: a deep survey. Expert Syst. Appl. **164**, 113794 (2021)

14. Agrawal, S.C., Jalal, A.S., Bhatnagar, C.: Recognition of Indian sign language using feature fusion. 4th International Conference on Intelligent Human Computer Interaction: Advancing Technology for Humanity, IHCI 2012, pp. 1–5 (2012)

15. Cooper, H., Holt, B., Bowden, R.: Sign language recognition. Visual Analysis of Humans: Looking at People, pp. 539–562 (2011)

16. Munnaluri, V., Pandey, V., Singh, P.: Machine learning based approach for indian sign language recognition. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES), pp. 1128–1132 (2022)

17. Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: 2014 Fourth international conference on advanced computing & communication technologies pp. 5–12 (2014)

18. Wiryana, F., Kusuma, G.P., Zahra, A.: Feature extraction methods in sign language recognition system: a literature review. 1st 2018 Indonesian Association for Pattern Recognition International Conference, INAPR 2018, pp. 11–15 (2019)

19. Madhiarasan, D.M., Roy, P., Pratim, P.: A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets. arXiv preprint arXiv:2204.03328 (2022)

20. Hochgesang, J.A., Miller, M.T.: A celebration of the dictionary of American Sign Language on linguistic principles: Fifty years later. Sign Language Studies **16**(4) (2016)

21. Sridhar, A., Ganesan, R.G., Kumar, P., Khapra, M.: INCLUDE: A large scale dataset for indian sign language recognition. In: Proceedings of the 28th ACM international conference on multimedia, pp. 1366–1375 (2020)

22. Agrawal, S.C., Jalal, A.S., Tripathi, R.K.: A survey on manual and non-manual sign language recognition for isolated and continuous sign. International Journal of Applied Pattern Recognition **3**(2), 99–134 (2016)

23. Dhanjal, A.S., Singh, W.: An optimized machine translation technique for multi-lingual speech to sign language notation. Multimedia Tools and Applications **81**(17), 24099–24117 (2022)

24. Yang, H.D., Lee, S.W.: Robust sign language recognition by combining manual and non-manual features based on conditional random field and support vector machine. Pattern Recogn. Lett. **34**(16), 2051–2056 (2013)

25. Cheok, M.J., Omar, Z., Jaward, M.H.: A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybern. **10**(1), 131–153 (2017). https://doi.org/10.1007/s13042-017-0705-5

26. Mekala, P., Gao, Y., Fan, J., Davari, A.: Real-time sign language recognition based on neural network architecture. In: 2011 IEEE 43rd Southeastern symposium on system theory, pp. 195–199 (2011)

27. Giustolisi, B., Mantovan, L., Panzeri, F.: The roles of manual and non-manual cues in recognizing irony in italian sign language. Metaphor. Symb. **37**(4), 323–336 (2022)

28. Caridakis, G., Asteriadis, S., Karpouzis, K.: Non-manual cues in automatic sign language recognition. In: Proceedings of the 4th international conference on pervasive technologies related to assistive environments, pp. 1–4 (2011)

29. Al-Hammadi, M., Muhammad, G., Abdul, W., Alsulaiman, M., Bencherif, M.A., Mekhtiche, M.A.: Hand gesture recognition for sign language using 3DCNN. IEEE access **8**, 79491–79509 (2020)

30. https://www.kaggle.com/datasets/vaishnaviasonawane/indian-sign-language-dataset

31. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 science and information conference, pp. 372–378 (2014)
32. Barbhuiya, A.A., Karsh, R.K., Jain, R.: CNN based feature extraction and classification for sign language. Multimedia Tools and Applications **80**(2), 3051–3069 (2020). https://doi.org/10.1007/s11042-020-09829-y

33. Al-Qurishi, M., Khalid, T., Souissi, R.: Deep learning for sign language recognition: current techniques, benchmarks, and open issues. IEEE Access **9**, 126917–126951 (2021)

34. Aly, S., Osman, B., Aly, W., Saber, M.: Arabic sign language fingerspelling recognition from depth and intensity images. In: 2016 12th International Computer Engineering Conference (ICENCO), pp. 99–104 (2016, December)

35. Rafael, C.G., Richard, E.W.: Digital image processing, 3rd Edition. Prentice Hall (2002)

36. Mohana, R.M., Reddy, A.R.M.: Signer-Independent SLR System Using PCA and Multi-Class SVM. Praise Worthy Prize International Review on Computers and Software (IRESOE) **9**(12), 1946–1955 (2014)

37. Mali, D., Limkar, N., Mali, S.: Indian sign language recognition using SVM classifier. In: Proceedings of international conference on communication and information processing (ICCIP), pp. 1–9 (2019, May)

38. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol. 1, pp. 886–893 (2005, June)

39. Lilha, H., Shivmurthy, D.: Evaluation of features for automated transcription of dual-handed sign language alphabets. In: 2011 international conference on image information processing, pp. 1–5 (2011, November)

40. Miller, J.W.V., Shridhar, M.: Illumination-Invariant Image Processing. Machine Vision Handbook, pp. 543–564 (2012)

41. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision **60**, 91–110 (2004)

42. Gurjal, P.S., Kunnur, K.S.: Real time hand gesture recognition using sift. Int. J. Electr. Electri. Eng. **2**(3), 19–33 (2012)

43. Shukla, P., Garg, A., Sharma, K., Mittal, A.: A DTW and fourier descriptor based approach for Indian sign language recognition. In: 2015 Third International Conference on Image Information Processing (ICIIP), pp. 113–118 (2015, December)

44. Kalampokas, T., Papakostas, G.A.: Moment transform-based compressive sensing in image processing. In: Systems, Signals and Image Processing: 28th International Conference, IWSSIP 2021, Bratislava, Slovakia, June 2–4, 2021, pp. 96–107. Springer (2022)

45. Geetha, M.K., Manjusha, U.C.: A vision based recognition of indian sign language alphabets and numerals using B-spline approximation. International Journal on Computer Science and Engineering (IJCSE) **4**(3), 406–415 (2012)

46. Doval, A.: The People’s Linguistic Survey of India Sign Language (2013)

47. Rekha, J., Bhattacharya, J., Majumder, S.: Shape, texture and local movement hand gesture features for indian sign language recognition. In: 3rd international conference on trendz in information sciences & computing (TISC2011), pp. 30–35 (2011)

48. Singha, J., Das, K.: Indian sign language recognition using eigen value weighted euclidean distance based classification technique. Int. J. Adv. Comp. Sci. Appli. **4**(2) (2013)

49. Ghotkar, A.S., Kharate, G.K.: Study of vision based hand gesture recognition using indian sign language. Int. J. Smart Sensing and Intelli. Sys. **7**(1), 96–115 (2014)

50. Pal, S.K., Ghosh, A., Kundu, M.K.: Soft computing and image analysis: features, relevance and hybridization. Soft Computing for Image Processing, 1–20 (2000)

51. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer **29**(3), 31–44 (1996)

52. Otiniano-Rodríguez, K.C., Cámara-Chávez, G., Menotti, D.: Hu and Zernike moments for sign language recognition. In: Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012, 2, pp. 1–5 (2012)
53. Saraswat, M., Arya, K.V.: Automatic facial expression recognition in an image sequence of non-manual indian sign language using support vector machine. In: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), 2, 267–275. Springer India (2012)

54. Singha, J., Das, K.: Recognition of Indian Sign Language in Live Video. Int. J. Comp. Appl. **70**(19) (2013)

55. Khurana, G., Joshi, G., Kaur, J.: Static hand gestures recognition system using shape based features. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–4 (2014)

56. Sawant, S.N., Kumbhar, M.S.: Real time sign language recognition using pca. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1412–1415 (2014)

57. Das, S., Talukdar, A., Sarma, K.: Sign language recognition using facial expression. Procedia Computer Science. **58**, 210–216 (2015)

58. Kishore, P.V.V., Prasad, M.V., Prasad, C.R., Rahul, R.: 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, pp. 34–38 (2015)

59. Tripathi, K., Baranwal, N., Nandi, G.C.: Continuous dynamic Indian Sign Language gesture recognition with invariant backgrounds. In: 2015 international conference on advances in computing, communications and informatics (ICACCI), pp. 2211–2216. IEEE (2015)

60. Sharma, K., Joshi, G., Dutta, M.: Analysis of shape and orientation recognition capability of complex Zernike moments for signed gestures. In: 2015 2nd international conference on signal processing and integrated networks (SPIN), pp. 730–735. IEEE (2015, February)

61. Ahmed, W., Chanda, K., Mitra, S.: Vision based hand gesture recognition using dynamic time warping for Indian sign language. In: 2016 international conference on information science (ICIS), pp. 120–125. IEEE (2016, August)

62. Prasad, M.V.D., Kishore, P.V.V., Kiran Kumar, E., Anil Kumar, D.: Indian sign language recognition system using new fusion based edge operator. J. Theoret. Appl. Info. Technol. **88**(3) (2016)

63. Tripathi, K., Nandi, N.B.G.: Continuous Indian sign language gesture recognition and sentence formation. Procedia Computer Science **54**, 523–531 (2015)

64. Patil, S.B., Sinha, G.R.: Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT). Journal of The Institution of Engineers (India): Series B **98**(1), 19–26 (2016). https://doi.org/10.1007/s40031-016-0250-8

65. Kumar, P., Roy, P.P., Dogra, D.P.: Independent Bayesian classifier combination based sign language recognition using facial expression. Inf. Sci. **428**, 30–48 (2018)

66. Badhe, P.C., Kulkarni, V.: Indian sign language translator using gesture recognition algorithm. In: 2015 IEEE international conference on computer graphics, vision and information security (CGVIS), pp. 195–200. IEEE (2015)

67. Nagendraswamy, H.S., Kumara, B.C., Chinmayi, R.L.: Indian sign language recognition: an approach based on fuzzy-symbolic data. In: 2016 international conference on advances in computing, communications and informatics (ICACCI), pp. 1006–1013. IEEE (2016)

68. Katoch, S., Singh, V., Tiwary, U.S.: Indian Sign Language recognition system using SURF with SVM and CNN. Array **14**(1–9) (2022)

69. Patel, U., Ambekar, A.G.: Moment based sign language recognition for indian languages. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–6. IEEE (2017)

70. Adithya, V., Vinod, P.R., Gopalakrishnan, U.: Artificial neural network based method for Indian sign language recognition. In: 2013 IEEE conference on information & communication technologies, pp. 1080–1085 (2013)
71. Gangrade, J., Bharti, J.: Vision-based hand gesture recognition for Indian sign language using convolution neural network. IETE Journal of Research, 1–10 (2020)

72. Wadhawan, A., Kumar, P.: Deep learning-based sign language recognition system for static signs. Neural Computing and Applications 32(12), 7957–7968. Springer (2020)

73. Sharma, P., Anand, R.S.: A comprehensive evaluation of deep models and optimizers for Indian sign language recognition. Graphics and visual computing 5, 200032 (2021)