## 1 Introduction  
In the past, the artificial intelligence field has accomplished a quantum leap. Currently, studies utilizing artificial intelligence are conducted and applied to various fields [1; 2; 3]. Especially, owing to the improvement in computing power, the areas of utilization constantly become wider in the field of computer vision. Furthermore, previous studies performed research related to image-text recognition [4; 5; 6; 7]. Similarly, there are studies on optical character recognition (OCR), which is considered the representative technique of today for detecting texts utilizing image data [8; 9; 10; 11]. Text and handwriting recognition started with the recognition of numbers in Latin text in the late-1950s and is expanding to include various languages, such as Chinese, Japanese, Arabic, and Persian. Currently,the methods for recognizing new languages are becoming more accurate [12; 13]. The recognition accuracy has increased through different recognition models because of the differences in the form of each language and in note-taking methods. Recently, the demand for handwriting recognition, such as the automation of mail sorting and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using convolutional neural networks (CNN), which show outstanding performance, have been applied to handwriting recognition [14; 15]. [PERSON] et al. [16] proposed an efficient CNN-based text recognition for Arabic handwriting. Their study used data augmentation to enhance the accuracy of the model. In addition, the method used dropout layers to resolve the problem of data overfitting and appropriately changed the activation function to overcome the vanishing gradient problem. [PERSON] and [PERSON] [17] proposed a hybrid neural network training algorithm for English handwriting OCR. It removed the noise of the input image and adjusted the size of the image using a median filter. In addition, feature sets and positional and structural descriptors were extracted from the input image. After the feature sets were extracted, the proposed FLM-based neural network recognized the handwritten text. FLM is a method that combines the firefly algorithm with the Levenberg-Marquardt (LM) algorithm for neural network training [18]. The proposed FLM-based neural network was integrated into a feedforward neural network, and, based on the size of the training data, the number of hidden neurons, and the number of hidden layers, 95% accuracy was achieved. [PERSON] et al. [19] proposed a CNN-recurrent-neural-network (RNN)-based license plate recognition method. Their study investigated the combination of CNN and RNN, that is, bi-directional long short-term memory (BLSTM). Since CNN has high recognition power, it has been used in feature extraction, while BLSTM has been used in the extracting function of the context information based on past information. In addition, for the classification of license plates, dense cluster-based voting was proposed to differentiate the foreground from the background. The methods of these studies can achieve fast speed and high accuracy regarding the input data, can objectively and accurately judge complicated images, and can provide various services. However, the recognition rate is low, and there is a limitation to the recognition of various styles of writing if they fall outside a certain standard, or there is a modification of the image. In addition, there is a disadvantage that a large volume of labeled data is needed for the classification work [20; 21]. Moreover, it is difficult to maintain spatial and structural consistency for the results of image segmentation. These technical drawbacks cause problems, such as unclear image outlines and incomprehensible small-area segmentation. In addition, to accomplish a high recognition rate in the handwriting recognition field, a deep structure CNN has been used.