## 1 Introduction
Images with diverse set textures, shapes, and colors require a hybrid features model to include textured features, such as overlapped multi-oriented tri-scale local binary pattern (OMTLBP) [13], robustness-driven hybrid descriptor (RDHD) [14], and discriminative local features of overlapping stripes (DLFOS) [15]. Deep neural networks developed to detect textual characters of different languages include BLPnet [16], HMM-DNN [17], distance-based edge linking (DEL) [18], and KDANet [19]. The DNN model recognizes the character of specific language depending on text mining methods to bag-of-words [20; 21]. The visual and vocal reviews of the customer transformed into textual shape require a pre-processing stage of voice and text character recognition. The images and voice pre-processing sub-stages are included for the precise transformation of data into textual shape [22]. Visual review can be based on either video or image data. In the case of video, vocal analysis can be used for classification, while, in the case of an image, review classification requires a distinctive feature set that remains robust and invariant to variations in view angle, scale, and illumination [23]. Text images, which consist of characters with various shapes and textures, necessitate texture and shape representation models [23]. Due to intra-class variations in color, shape, and illumination, image identification becomes challenging, and, therefore, stable features are considered. Various types of neural networks, such as AlexNet, ResNet, DagNet, Vgg-16, InceptionV3, GoogleNet, and Yolo, have been developed for the classification of the ImagNet dataset [24]. These models have achieved promising performance in image classification, but they require extensive data for fine-tuning new categories. Therefore, handcrafted statistical frameworks are associated with a pre-processing step for feature extraction. Textured images can be represented using local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) [25]. LBP and GLCM are chosen because of their noise robustness and photometric invariance. To represent the input image as a whole for object recognition, we use the concatenated shape of all three features--color, texture, and shape. The robust features require distinctiveness and a stable classification approach for precise categorization of customer reviews. Researchers have employed various isolated classifiers, including multi-class SVM, discriminant analysis, ensemble, decision tree, and KNN, to obtain the estimated label of the test image [26]. Kernel improves the classification performance of existing classifiers by mapping non-linear data into high-dimensional space [27]. The kernel function estimates the similarity between pairs of samples in the transformed spaces. Classifier kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid kernels [28]. Careful selection of kernels enhances the categorization precision of the model; the RBF kernel outperforms other kernel types in many classification problems, whereas the polynomial kernel can perform well in image recognition tasks [29]. The performance of the classification models has been validated by comparing the estimated label with the true label of the test data. The label represents the class name of the customer review. Regarding the set of true labels and estimated labels, when compared, the classification performance is evaluated and presented in the shape of a confusion matrix. The true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) obtained from the set of true and predicted labels are used to validate the performance of the image recognition frameworks. The industrial sectors and product manufacturers encourage constructive feedback from reviewers; however, initially, when purchasing a product, consumers can hardly provide valuable feedback related to the specific product. However, after using the product for a specific time period, reviewers can initiate effective feedback.