# Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
## 1 Introduction
To find a specific shape in the diagrams, template matching was also used with a symbol shape incorporated as part of the prior information [14]. In [15], a threshold-based object-detection algorithm was proposed for binary images. For graphical symbol recognition using a machine learning approach for engineering diagrams, there is very little previous research. In the past, the template matching method [16] has been famously used to find a specific shape within an image by sliding the template across the entire window. In all slides, the template calculated the similarity using convolutional estimation. However, this method has an inherent disadvantage; when it estimates the similarity within a specific region, it uses the Euclidian distance. This metric is intuitive, and the method is convenient to apply, but it cannot consider the images with large numbers of dimensions. It judges similarity using only quantitative calculations. Consequently, when it sets a high threshold value, it misses the shapes in the image owing to the stringent criteria. In contrast, when it sets a low threshold value, it detects unsuitable shapes because of its naive criteria. This makes it difficult to find a suitable threshold value and improve its detection performance. Furthermore, almost all images in the real world contain noise or resolutions inadequate for the implementation of machine vision methods. In terms of industrial scenarios, the template matching-based method is not suitable for object detection in engineering diagrams. In electrical EDs, circuit symbols can be recognized with morphological operations and geometric analyses [17]. A convolutional neural network can be used to recognize the symbols in hand-sketched engineering diagrams and convert them to a computer-aided design (CAD) program [18]. Adapting the Hopfield model, an iterative neural network model was implemented for symbol recognition by employing a prototype [19]. One symbol-classification method applied to P&IDs considered class decomposition using k-means clustering [20]. Fully convolutional networks (FCN), which are an end-to-end network for pixel-wise prediction, were first applied as object detection for P&IDs in [21]. In [22], the author proposed a method to extract various objects, including symbol, characters, lines, and tables in a P&ID, using a machine vision method containing deep learning architecture. To reduce human effort while validating the CAD documents, the P&IDs attributed by the graph form were trained by a neural network and predicted the components vector which represents the diagram flow in [23]. However, these previous approaches toward symbol recognition for P&IDs could not control the detection quality effectively for the symbols in the diagrams. Their applicability to real diagrams could not be confirmed because unpredictable objects were detected during the region proposal. This makes it difficult to recognize the target symbol owing to the characteristics of engineering diagrams made up of quasi-binary components. In object detection, certain algorithms have delivered remarkable performances in recent years. The appearance of convolution networks has heralded significant improvements in image-classification problems. Even so, unlike basic image classification, object detection requires the solution not only of multi-labeled classification problems but also the bounding boxes for proposed regions in a digital image. To achieve this, networks for object detection employ a region proposal network (RPN), which plays the role of finding the symbol-region candidates, before classifying images. In a region-based convolutional neural network (R-CNN) [24], a selective search algorithm [25] was used for the RPN, and the boxed images were fed to the network for classification. RPN extracts numerous boxes from an image, considering the colors, scale, boundary, etc., of the object. The proposed regions are reshaped before being fed into the convolutional network for image classification.