Segmentation-based Fractal Texture Analysis Research Articles

Kinematics Model Estimation of 4W Skid-Steering Mobile Robots Using Visual Terrain Classification

Accurate real-time kinematics model is very important for the control of a skid-steering mobile robot. In this study, the kinematics model of the skid-steering mobile robots was first designed based on instantaneous rotation centers (ICRs). Then, the extended Kalman filter (EKF) technique was applied to obtain the parameters of ICRs under the same specific terrain online. To adapt to different terrain environments, the fractal dimension-based SFTA (segmentation-based fractal texture analysis) method was used to extract features of different terrains, and the k-nearest neighbor (KNN) method was used to classify the terrains. In the case of real-time terrain recognition, the filter parameters of the EKF for estimating the ICRs are adjusted adaptively. Experiments on a real skid-steering mobile robot show that this method can quickly estimate the kinematics model of the robot in the case of terrain changes, and can meet the needs of practical applications. The average error of odometer estimation based on visual terrain classification is 0.06 m, while the average error of odometer estimation without terrain classification is 0.14 m.

A robust IoT-based cloud model for COVID-19 prediction using advanced machine learning technique

Due to the rapid spread of COVID-19, an urgent need arose for a quick and accurate diagnosis approach. The well-known RT-PCR test is often unavailable and too costly for many suspected cases. In contrast, the chest X-ray scan proves to be a robust and efficient tool for promptly identifying the virus, using various cost-effective tests. This study proposes a hybrid intelligent model for identifying COVID-19 patients based on their chest X-ray scans. The proposed model consists of five phases. Firstly, the X-ray images are preprocessed using a median filter to enhance and remove noise. Secondly, robust invariant and high-level engineering features are extracted using five different feature extraction methods, namely (speeded up robust features (SURF), grey-level co-occurrence matrix (GLCM), histogram of oriented gradients (HoG), segmentation-based fractal texture analysis (SFTA), and local binary pattern (LBP) to help in rapid diagnosis. Then these features are fused to complement the gap of each other and to achieve the best performance. Thirdly, a deep convolutional neural network (DCNN) is used to classify the chest X-ray scans of infected COVID-19 patients. Then, the proposed model is compared with different machine learning techniques under different quality measures. Fourthly, an IoT platform composed of a mobile client application and a backend system is designed. The mobile client application captures the content from the patient side, handles these captured images, and prepares them for rapid sending to the backend side for further processing. The backend system receives the patient images from the client side and applies the proposed model to diagnose the COVID-19 patients. Finally, a cloud-based design for the platform utilizes the modern AWS services characterized by availability, elasticity, coverage, configurability, and reliability. The results show that the proposed model has a good accuracy of around (99.31%) and outperforms the other models. Therefore, the proposed model could be used for real-time automatic early recognition of COVID-19 cases.

Assessment of high-quality counterfeit stamp impressions generated by inkjet printers via texture analysis and likelihood ratio

High-quality counterfeit stamp impressions made by inkjet printers remain challenging in questioned document examination and forensic analyses. A dataset comprised of various printed stamp impressions, using ten options of conditions and materials, and hand stamped impressions was generated. In this paper, we report printed impressions in pure color and high-quality printing mode are very similar to hand stamped impressions in terms of their microscopic characteristics. These similarities may lead to incorrect conclusions via traditional identification methods. Here, we proposed a method for identifying counterfeit stamp impressions via texture features and image quality parameters extracted from impressions. First, the statistical analysis methods were used to verify a significant difference between the printed and hand stamped impressions. Principal component analysis (PCA) was used to show the variation between the impressions, and the differences between printed and hand stamped impressions were obvious in the three-dimensional plot. After filtering the background of the stamp impressions, image processing analysis was introduced to extract features of gray level co-occurrence matrix (GLCM), segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and image quality metrics (IQM), which were used to characterize the stamp impressions. Finally, specific cases were simulated by random selection, based on the dataset of stamp impressions, and an evaluation system for stamp evidence was established to calculate the likelihood ratios (LRs) under two alternative hypotheses. The likelihood ratio interprets calibrated evaluations on the strength of stamp impressions as evidence. We can also balance these LRs against the rates of misleading evidence with a reasonable performance (equal error rate = 0.048). This paper provides a system to differentiate high-quality printed and hand stamped impressions with reasonable performance.

Binary and Multi-Class Malware Threads Classification

The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample. Recently, a few techniques for analyzing malware quickly have been put out. However, there remain numerous difficulties, such as the low classification accuracy of samples from related malware families, the computational complexity, and consumption of resources. These difficulties make detecting and classifying malware very challenging. Therefore, in this paper, we proposed an efficient malware detection and classification technique that combines Segmentation-based Fractal Texture Analysis (SFTA) and Gaussian Discriminant Analysis (GDA). The outcomes of the experiment demonstrate that the SFTA-GDA produces a high classification rate. There are three main steps involved in our malware analysis, namely: (i) malware conversion; (ii) feature extraction; and (iii) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are then extracted from gray-scale images in the feature extraction step. Finally, the classification is carried out by GDA and Naive Bayes (NB). The proposed method is evaluated on a common MaleVis dataset. The proposed SFTA-GDA is the effective choice since it produces the highest accuracy rate across all families of the MaleVis Database. Experimental findings indicate that the accuracy rate was 98%, which is higher than the overall accuracy from the existing state-of-the-art methods.

A Steganalysis Classification Algorithm Based on Distinctive Texture Features

Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the “non-stego” image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods.

Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN

The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, and decision support are combined to design of a smart agriculture assistance system. The proposed system has a sensor pack that provides continuous data capture of temperature records, air and soil moisture and a camera for obtaining near-infrared (NIR) images of the plant leaves for use with an AI decision support system. We identify twelve types of vegetation for the study, out of which five disease classes of the tomato leaves are categorized using a Convolutional Neural Network (CNN). The work also includes experiments conducted with multiple clustering-based segmentation methods and some features namely Gray level co-occurrence matrix (GLCM), Local binary pattern (LBP), Local Binary Gray Level Co-occurrence Matrix (LBGLCM), Gray Level Run Length Matrix (GLRLM), and Segmentation-based Fractal Texture Analysis (SFTA). Out of several AI tools, CNN proves to be effective in providing automated decision support for classifying the plant leaf disease types through a cloud server that can be accessed using an app. Extensive on-field trials show that the system (VGG16 CNN, GLCM and a fuzzy based clustering) is effective in hot and humid conditions and proves to be reliable in identifying disease classes of certain vegetable types, certain usable vegetation cover of farmland and regulation of watering mechanism of crops.

Forgery detection algorithm based on texture features

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.

Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques

The performance of machine learning (ML)-based path loss models relies heavily on the data they use at their inputs. Feature engineering is, therefore, essential for the model's success. In this letter, at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the segmentation-based fractal texture analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm, thus, delivers a new set of features, based on the image's texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, ML algorithms, and performance metrics.

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.

Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand‐crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U‐Net deep learning model. The hand‐crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand‐crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand‐crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand‐crafted features (ii) classification using fusion of hand‐crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand‐crafted & deep microscopic feature's fusion provide better results compared to only hand‐crafted fused features.

Segmentation-Based Fractal Texture Analysis (SFTA) to Detect Mass in Mammogram Images

ABSTRAK Di Indonesia, kasus kanker paling banyak adalah kanker payudara yaitu 58.256 kasus atau 16,7% dari total 348.809 kasus kanker. Dibutuhkan suatu sistem yang dapat membantu pakar untuk mendeteksi kanker payudara pada wanita dengan mengindentifikasi citra mammogram. Keabnormalan dapat dideteksi dari massa pada mammogram yaitu area dengan pola tekstur dan bentuk serta batas tertentu. Berdasarkan hal tersebut maka dibuat sebuah sistem yang dapat mendeteksi massa kanker pada citra mammogram menggunakan Segmentation-Based Fractal Texture Analysis (SFTA). Tahapan pertama akuisisi citra, dilanjut dengan segmentasi menggunakan k-means dan thresholding. Hasil dari segmentasi citra dilakukan tahapan morfologi menggunakan opening dan masking. Setelah itu dilakukan ekstraksi fitur SFTA, dan klasifikasi Support Vector Machine (SVM). Hasil pengujian penelitian ini didapatkan nilai akurasi sebesar 90%, presisi sebesar 87,75%, recall sebesar 93,33%dan f1-score 90,32% dengan nilai number of threshold (nt) SFTA adalah 3 Kata kunci: mammogram, SFTA, kanker payudara, klasifikasi ABSTRACT In Indonesia, the most cancer cases were breast cancer, namely 58,256 cases or 16.7% of the total 348,809 cancer cases. A system is required to assist the expert in detecting breast cancer in women by identifying mammogram images. Abnormalities in a mammogram are determined in part of texture with a particular form and specific limit, usually called a ‘mass.’ Image acquisition is perceived as the first step, followed by segmentation using the k-means and the thresholding. Image segmentation undergoes the morphological analysis steps using opening and masking methods, after feature extraction processing by SFTA, using Support Vector Machine (SVM) for classification processing. The obtained research result revealed an accuracy value of 90%, a precision value of 87.75%, a recall value of 93.33%, and an F1-Score of 90.32%, with the number of thresholds (nt) of SFTA amounting to 3. Keywords: Breast cancer, Mammogram, Classification, SFTA

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

A modified fractal texture image analysis based on grayscale morphology for multi-model views in MR Brain

<span>This paper presents a modified fractal texture feature analysis with the use of grayscale image morphology for automatic image classification of different views in MR brain images into normal and abnormal. This main contribution of this approach is a reduction of the total number of a threshold value, and the number of image decomposition, in which only the number of extract threshold value two or three are enough for tumor region extraction - compared to four or more is required in the previous method of SFTA (segmentation based fractal texture analysis). This is achieved by pre-processing of hierarchical transformation technique (HTT), which make use of morphological image transformations with the desired structural element. From this decomposed images, mean, area, fractal dimension and selective shape features are extracted and fed into KNN and ensemble bagged tree classifiers. Finally, some of the post-processing is handled for tumor region extraction and tumor cells computation. It is found that this proposed approach has superior results in the segmentation of diseased tissue from normal tissue and the prediction of image classes in terms of accuracy with the less number of threshold extraction and image decomposition rather than the SFTA algorithm.</span>

Texture features in the Shearlet domain for histopathological image classification

BackgroundA various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning.MethodsIn this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB).ResultsOur feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%).ConclusionsOur proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity.

A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality

We propose a combination of image processing methods to detect ceramic tiles defects automatically. The primary goal is to identify faults in ceramic tiles, with or without texture. The process consists of four steps: preprocessing, feature extraction, optimization, and classification. In the second step, gray-level co-occurrence matrix, segmentation-based fractal texture analysis, discrete wavelet transform, local binary pattern, and a novel method composed of segmentation-based fractal texture analysis and discrete wavelet transform are applied. The genetic algorithm was used to optimize the parameters. In the classification step, k-nearest neighbor, support vector machine, multilayer perceptron, probabilistic neural network, and radial basis function network were assessed. Two datasets were used to validate the proposed process, totaling 782 ceramic tiles. In comparison with the other feature extraction methods commonly used, we demonstrate that the use of SFTA with DWT had a remarkable increase in the overall accuracy, without compromising computational time. The proposed method can be executed in real time on actual production lines and reaches a defect detection accuracy of 99.01% for smooth tiles and 97.89% for textured ones.

Rough sets and social ski-driver optimization for drug toxicity analysis

Background and objectives: Toxicity testing is an important step for developing new drugs, and animals are widely used in this step by exposing them to the toxicants. Zebrafishes are widely used for measuring and detecting the toxicity. However, measuring and testing toxicity manually is not feasible due to the large number of embryos. This work presents an automated model to investigate the toxicity of two toxicants (3, 4-Dichloroaniline (34DCA) and p-Tert-Butylphenol (PTBP)).Methods: The proposed model consists of two steps. In the first step, a set of features is extracted from microscopic images of zebrafish embryos using the Segmentation-Based Fractal Texture Analysis (SFTA) technique. Secondly, a novel rough set-based model using Social ski-driver (SSD) is used to find a global minimal subset of features that preserves important information of the original features. In the third step, the AdaBoost classifier is used to classify an unknown sample to alive or coagulant after exposing the embryo to a toxic compound.Results: For detecting the toxicity, the proposed model is compared with (i) three deterministic rough set reduction algorithms and (ii) the PSO-based algorithm. The classification performance rate of our model was ranged from 97.1% to 99.5% and it outperformed the other algorithms.Conclusions: The results of our experiments proved that the proposed drug toxicity model is efficient for rough set-based feature selection and it obtains a high classification performance.

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.

Textural and color descriptor fusion for efficient content-based image retrieval algorithm

In communication, medium images are used for various applications such as social websites, education, bio-medical field and industrial applications. Indexing and retrieval of such large image database pose a big problem. The content-based image retrieval (CBIR) approaches are used to select information from the input images using feature descriptors. In this article, CBIR algorithm is proposed with fusion of color and texture descriptors such as binary Gabor pattern (BGP), segmentation based fractal texture analysis (SFTA), edge histogram descriptor (EHD), Gabor wavelet texture features and fuzzy histogram-based descriptors. Laplacian score has employed to reduce feature vector dimensions. Retrieval rate is computed using different distance metrics. Experimentation was performed with ten different classes of 1000 images using Wang database. The simulation results indicate improvement in terms of precision compared with the existing techniques.

A Survey Paper on Gender Classification using Deep Learning

With technological advancements many small to large, simple to complex activities are automated. Growth of Artificial Intelligent techniques has eased the way we would look to solve the real-world problems. One such area which has recently gained lot of attention is the biometric analytics like Face Recognition, Fingerprint, voice etc. It involves extracting features such as face expressions, gender, age etc. Gender information plays a vital role in areas such as human computer interaction, crime detection, gender preferences, facial biometrics for digital payments etc. This paper proposes gender recognition using facial images and fingerprints with different algorithm like Visual Geometry Group-VGGNet16?, Segmentation based Fractal Texture Analysis (SFTA) etc.

Script Identification of Camera Based Bilingual Document Images Using SFTA Features

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

<i>Indic</i> script identification from handwritten document images

Script identification plays an important role in document image processing especially for multilingual environment. This paper hires two conventional textural methods for recognition of the scripts of the handwritten documents inscribed in different Indic scripts. The first method extracts well-known Haralick features from spatial grey-level dependence matrix (SGLDM) and the second method computes fractal dimension by using segmentation-based fractal texture analysis (SFTA). Finally, a 104-element feature vector is constructed from each page image by these two methods. The proposed technique is then evaluated on a total dataset comprising 360 handwritten document pages written in 12 Indian official scripts namely Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu and Roman. Experimentations using multiple classifiers reveal that multilayer perceptron (MLP) shows the highest identification accuracy of 96.94%. The encouraging outcome confirms the efficacy of customary textural features to handwritten Indic script identification.