# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 1 Introduction  
Scene text detection is an important and challenging task in computer vision [1; 2]. The goal is to accurately locate the text area in a scene image. This technology has a wide range of applications in image retrieval, scene analysis, blind navigation, and other fields [3]. Text found in natural scenes have different fonts, styles, and sizes. It is usually accompanied by geometric distortion, a complex background, and uncontrolled lighting. Therefore, natural scene text detection is still a very open research challenge. Early mainstream approaches focused on various heuristics that help detect characters or character components. The two most famous approaches are: (1) the maximum stable extremal region (MSER) [4; 5; 6]; and (2) the stroke width transformation (SWT) [7; 8]. MSER extracts character regions with a similar intensity whereas SWT assumes that the text component has a comparable stroke width. Both MSER and SWT must be combined with additional post-processing to produce reasonable text candidates. In recent works, various convolutional neural network (CNN)-based methods have been proposed to detect scene text [9; 10; 11; 12; 13; 14]. These efforts focus on reducing the number of handcrafted features or artificial rules in text detection. [PERSON] et al. [15] propose a text flow method using a minimum cost flow network to sort character CNN candidate detection, erroneous character deletion, text line extraction, and text line verification. [PERSON] et al. [16] propose a canny text detector using the maximum stable region, edge similarity, text line tracking, and heuristic rule grouping to calculate candidate characters. One of the most important CNN-based methods is a fully convolutional network (FCN). [PERSON] et al. [17] suggest using a FCN to obtain text block candidates, a character-centroid FCN to generate auxiliary text lines, and a set of heuristic rules based on intensity and geometric consistency to reject the wrong candidates. [PERSON] et al. [18] proposes a fully convolutional regression network (FCRN) that efficiently performs text detection and bounding-box regression at all locations across multiple scales in an image based on a FCN. All of these algorithms use FCN to generate text semantic segmentation detection results containing semantic segmentation information. Another method called multibox uses multiple default candidate boxes to calculate the position of text in an image. Since general object detection based on CNN has achieved remarkable results in recent years, scene text detection has been greatly improved by treating text words or lines as objects. High-performance methods for object detection such as a faster region-based convolutional neural network (R-CNN) [19], single shot multibox detector (SSD) [20], and you only look once (YOLO) [21] have been modified to detect horizontal scene text [10; 14; 22; 23] and have been greatly improved. In this paper, the outside mutual correction (OMC) algorithm is proposed. Existing fusion approaches use semantic segmentation as a module for extracting features inside a multibox detector, this is referred to as inside feature extraction (IFE). During IFE processing, feature maps extracted by semantic segmentation are enlarged first and then reduced, which usually introduces noise and reduces the accuracy of detection. In our proposed algorithm, semantic segmentation and multibox are processed in parallel, and the text detection results are mutually corrected. Thus, the bounding box enhancement module (BEM) and the semantic bounding box module (SBM) were designed. The proposed algorithm inherits the advantages of these methods and obtained more accurate text detection results through OMC. The rest of the paper is organized as follows: In Section 2, we provide a brief review of the related theories, including single shot multibox and the FCN. In Section 3, we describe the details of our proposed algorithm. In Section 4, we present the experimental results on benchmarks and comparisons to other scene text detection systems.