# Chinese Character Boxes: Single Shot Detector Network for Chinese Character Detection
## 1 Introduction  
Many studies have been conducted to solve optical character recognition (OCR) problems but challenging problems still remain due to various external factors such as the type and boldness of the handwriting, the size of the document, the degree of damage to the document, and the background of the document. In the case of handwritten documents, various noise can occur due to the small size of the text, heavy noise, dense text, and preservation environment. In particular, the preservation environment can cause damage or severe noise. These problems are very difficult to detect and recognize using conventional methodologies. The document covered in this paper is an old document written in caoshu. Caoshu is one of the typefaces of Kanji which corresponds to a curve-oriented cursive and is in the form shown in Figure 0(b). Caoshu differs in shape according to the author and has a different form from that of Figure 0(a) which is the standard Chinese character type today. Old documents are of high historical value written in a handwritten form and still remain untranslated because of the difficulty and time consuming to translate. It will be a meaningful to discover the history that has not yet been revealed by accelerating the translation of these documents through deep learning which has shown good performance in various fields. This paper proposes a Chinese character boxes (CCB) network for character area detection based on a single-shot multibox detector (SSD) [1] called CCB-SSD to detect each character of an old document. In addition, this paper introduces the detection results based on the number of default boxes and aspect ratios used in SSD and augmentation methods for the cropped data-sets and document type data-sets. Also, a novel non-maximum suppression (NMS) algorithm was proposed to solve the overlap problem caused by the use of several default boxes. A new performance evaluation index, false positives per character (FPPC) was introduced and compared with false positives per image (FPPI) which evaluates the per-image detection performance. The contributions of paper are as follows. * CCB-SSD network was proposed for Chinese character detection based on a single-scale structure that can be trained by end-to-end. This can make the network deep enough without additional steps such as deconvolution-based up-sampling and feature map concatenation to overcome the limitations of conventional methods. Therefore, semantic information can be fully exploited and even smaller characters can be detected as a result. * Using the proposed single-scale structure and NMS, the problem of the existence of unnecessary layers and the relationship between the classifiers was solved and the problem of overlap caused by the use of several default boxes was resolved. * The FPPC, a new evaluation index that can be a more objective indicator than the FPPI was proposed for the detection of Chinese characters in old documents. In addition, an augmentation method of data-sets with document type and cropped data type was introduced. The remainder of this paper is organized as follows. Section 2 briefly introduces previous studies related to the detection and recognition of Chinese characters. Section 3 outlines the proposed detector networks called CCB-SSD and training methods. Section 4 introduces various experimental results and finally Section 5 reports the conclusions. In recent years, deep learning has been active in many areas of computer vision and deep learning-based methodologies have been explored in the field of character detection, recognition and translation. [PERSON] et al. [19] used deep convolutional neural networks (CNN) to classify different types of letters. If the network is deep enough for the classification problem, the size of the fully connected layer will not affect the performance. [PERSON] et al. [20] used four networks, AlexNet [21], VGG [22], LeNet [23], and ZFNet [24], and a single network that was deep enough to assume that the fused network did not have significant performance improvements.