## 2 Literature Review
The researcher in [27] presents a hybrid feature selection model with a discriminant feature distribution analysis-based feature subset evaluation. This method is focused on online bearing fault diagnosis in induction motors. The proposed model better performance than the state-of-the-art average distance-based approaches by a performance margin of about 5%. Paper [28] has presented an overview of automatic writer identification modules of an embedded biometric system based on small-scale handwritten data. They evaluated both static and dynamic features and proposed a new feature selection algorithm based on likeness coefficients. The identification result of 95% validates the use of handwriting in embedded personal identification devices. However, this system focused only on the small-scale handwriting samples of single handwritten words. The author considered the minimum distance classifier which is sensitive to the differences in variance among the categories. In [29], the authors have pointed out future trends and challenges in biometric research on signature and handwriting. Special emphasized the use of handwriting signals not only on biometric traits but also on e-security and e-health. Some challenges are identified which should attract the interest of the research community towards a more secure society. [PERSON] et al. in [30; 31] proposed classic approaches emphasizing on extracting the desirable features. Authors in [30] proposed a Block Wise Local Binary Count (BW-LBC) operator stimulated by traditional LBP that characterizes multiple histograms. Based on the distribution of the pixels in small blocks, the histograms are generated. They utilized the nearest-neighbor classification using the Hamming distance and presented that their approach is better than the modern approaches. For writer identification, the authors in [31], proposed another classical feature extraction method. Their descriptor illustrates a salient feature for local writing structure and is applied to small connected regions of the sample. These feature maps have been used as inputs for the nearest neighbor classifier to classify the query writer. These two works show that traditional features are still useful for writer identification and superior in some tasks. However, as the authors of this research are resizing the handwriting images, there is a possibility of losing the writers' personalities due to stretching the images. He and [PERSON] have recently proposed two deep architectures [32; 33]. The former one represents the handwriting which contains two implicit and explicit information where the explicit information refers to the length, number of characters, and lexical contents of single words. In contrast, the implicit information is the writer's behavioral information that can be used for author identity. The author proposed using both implicit and explicit information so that explicit information can be used along with the implicit features to make extra information. The proposed CNN architecture is based on the AlexNet, including two parallel pathways. However, a challenging issue of this research relies on resizing handwriting patches into 120 \(\times\) 140 \(\times\) 1, where such an affine transformation can lose the writer's intrinsic information. In [34], the authors represent a deep learning framework for offline text-independent writer identification based on the conjugation of the deep and traditional features. Proposed deep architecture is an extended version of ResNet, which is done using the auxiliary information of handwriting thickness descriptor (HTD). The HTD computed the thickness of handwriting as an essential and preliminary feature for human handwriting analysis. However, the authors do not resize the handwriting images, rather they suggest to crop patches of the handwriting and propose a descriptor to explain each handwriting image regarding the script thickness. Therefore, their model is a conjugating approach to fill the gap of using classical and modern features and achieves appropriate results.