## 1 Introduction  
In recent years, the field of handwriting has attracted interest from various aspects, such as biometrics [1] and the medical field [2]. In addition, handwritten characters can be obtained from a variety of sources such as paper documents, images, touch screens, and other devices. This makes the data easy to collect and suitable for classification. Furthermore, since handwriting is something that everyone uses every day in school, it is a method that is less stressful for people. There are few studies on handwriting classification for adults and children, and most of the studies are on the classification of face recognition [3], age groups [4], age, gender, and nationality [5], gender [6, 7], gender and handedness [8], detection of alcohol [9], and Parkinson's disease (PD) [10] based on handwriting images. There are two types of handwriting data: offline and online. The input data collected using a scanning machine are called \"offline\", whereas input data obtained using a pen tip are called \"online\" [11]. In our research work, we used the online-based handwritten database. Moreover, a single writer's handwriting may be unique or differ slightly, but the handwriting of a child and adult must always be different. Most forensic handwriting analysis is based on the inspection of specific character shapes, character ligatures, size, pen lift, pen pressure, speed, letter spacing, etc., to identify a suspected person. Age group detection will be a great solution before detecting the actual suspected person in forensic analysis. It will give additional evidence about the suspected person's age. Currently, there are many applications of handwritten recognition, for example, signature authentication used in industrial applications [2], authenticating of criminal investigations in a court of justice [12,13], document examinations [14], and so on. The most difficult aspects of handwriting identification are distortions and pattern variations; feature extraction is of supreme importance. Handwritten forensic analysis or handwriting recognition using machine learning (ML) algorithms can be a great solution to classify adults and children based on their handwritten text and handwritten pattern. [PERSON] et al. (2004) proposed support vector machine (SVM) with some kernels for online handwritten recognition [15]. They showed that at the character level, the SVM recognition rate was dramatically better due to the use of maximizing boundaries in the decision function. The only problem with this algorithm was storing large support vector for a huge training character that requires a larger memory size. [PERSON] et al. (2014) proposed k-nearest neighbors (k-NN) for recognizing handwritten digits based on structural features, which does not require thinning operation and size normalization approach [16]. [PERSON] et al. (2018) implemented neural networks (NN) and their variants to recognize handwritten digits. The survey details some existing techniques implemented for handwritten digit recognition (HWDR) being carried out [17]. [PERSON] et al. (2019) [18] also proposed convolutional neural networks (CNNs) to distinguish previous work to recognize handwritten characters using some data augmentation from works using the original dataset out-of-the-box [19,20]. They provided the most extensive and updated survey of the MNIST and EMNIST datasets and achieved the lowest error rate. [PERSON] et al. (2019) [2] applied logistic regression to predict PD based on handwritten recognition. They utilized the publicly available PD database and extracted secondary kinematic handwriting features from the dataset. It is being studied not only for personal identification but also in the medical field. The limitation of their proposed model was that they used small sample size of the dataset and lacked control in the study design. As for all the limitations of handwriting recognition, Japanese handwritten character recognition is complex due to the various types of writing styles, characters, and confusion among similar characters.