# Compact and Accurate Scene Text Detector
## 1 Introduction  
Optical character recognition (OCR) is the task of finding and recognizing potential characters (or texts) from given optical signals or images. OCR has received attention from academic and industrial fields because as it can be used for various services (e.g., reading services for blind people, license plate recognition). OCR has two steps: detection and recognition. The detection step detects the region or boundaries of text characters, and the recognition step recognizes what the characters are. For example, given an advertising picture that contains the text 'cola', the detection step finds the boundary of the text from the scene, and the recognition step identifies that the found boundary consists of the sequence of 'c'-'o'-'-'a'. It is obvious that better text detection will contribute to better text recognition. Scene text detection finds word boxes (or word regions) from scenes. There have been studies on scene text detection, which fall into two categories: (1) a segmentation-based approach, and (2) a regression-based approach. Studies on segmentation-based approaches mainly adopted fully convolutional network (FCN) [1], requiring further post-processing to transform the output in to the form of word boxes [2; 3; 4]. On the other hand, studies on regression-based approaches mainly used single shot detector (SSD) [5], faster region-based convolutional neural networks (Faster R-CNN) [6], or mask region-based convolutional neural networks (Mask R-CNN) [7]. These studies commonly adopted anchor boxes to capture the desired object with a particular aspect ratio [8; 9; 10]. Existing studies on both approaches sought better effectiveness by increasing the model complexity, and achieved about an 80\(\sim\)90% F1 score for the text detection. Effectiveness has traditionally been the main focus, and so improved efficiency has been neglected (e.g., memory usage, inference time). According to the recent report of Gartner [11], edge computing is one of the top 10 strategic technology trends. Edge computing places services closer to the information sources; for text detection, the scene data are collected from user devices (e.g., cellphones), so textdetection services might be provided by these devices. For this, it is necessary to develop efficient models without degrading effectiveness. There have only been a few studies aimed at developing efficient models for text detection; these efficient models are fast (less inference time), compact (small number of parameters), and cost-efficient (e.g., low floating-point operations per second (FLOPS)). In [12], efficient and accurate scene text detection (EAST) was introduced to perform scene text detection using Pvanet [13] as a backbone and standard convolution layers as a decoder. In [14], a similar approach to the above was included, but the backbone was Residual network (Resnet) [15], which is much larger than the Pvanet. In [10] visual geometry group network (VGGnet) was used [16], which is also a large backbone. Although these studies exhibited successful results, there are two goals that need to be considered: (1) improving the backbone model and (2) improving the decoder. EAST [12] has the smaller backbone (Pvanet) compared to the other studies, so its efficiency is the best among them. Therefore, it is worth investigating other potential backbone models such as MobileNet [17] and MobileNetV2 [18]. The decoder has a huge impact on the model effectiveness as well as the efficiency, so it is important to design the decoder carefully. However, the decoders in existing studies usually consist of traditional convolutional layers. In this paper, we carefully design a novel decoder, namely _Balanced decoder_, that achieves better effectiveness while maintaining good efficiency. We also investigate a better backbone model through experimental comparisons. For efficient text detection, this paper proposes a new model, which _Compact and Accurate Scene Text detector_ (CAST), that is a MobileNetV2-based network with the Balanced decoder.