## 1 Introduction  
Digital instruments are widely used in the fields of industrial control, device display and testing for their high precision, strong readability and easy maintenance [1]. However, most of digital instruments are lack of communication interface due to information security, cost control and application environment, which unable to realize automatic acquisition and transmission of digital number for subsequent information processing and data management. In this way, the recognition requirement of digital instruments is only relying on artificial reading by worker, which is inconvenience for information management in real-industrial scenarios. Specifically, there are many uncertain factors in artificial reading, and the result is easy to be interfered by subjective factors, resulting in low efficiency and low accuracy [2, 3, 4]. In addition, some high-risk environment with high temperature, high pressure, high radiation and strong corrosion is not suitable for manual work [5, 6]. Therefore, it is of great practical value to explore an efficient, accurate and robust method to recognize digital instrument reading automatically. As with the regular process of Optical Character Recognition (OCR), the core steps of digital instrument reading recognition are mainly divided into two stages, including reading detection and reading recognition. As the terminal step of the automatic recognition system of digital instruments, the quality of the recognition method directly determines the final recognition rate. Therefore, the recognition method has also received a lot of attention from scholars and experts. However, most of the current related studies only focus on a single instrument type, such as the digital multimeter [7; 8], the ammeter and voltmeter [5], the methane detecting instrument [9], the electric energy meter [10], and so on. Additionally, some studies are only applicable to specific application scenarios with superior imaging conditions, such as requiring the relative position of the camera and the instrument to be fixed [6; 11]. All of the above methods work well under certain circumstances, but they are difficult to cope with the recognition needs of real-industrial scenarios in which multiple instrument types are mixed. In the real world, the reading strings of the captured images are usually non-standardized with variable-length, due to different instrument locations, the difficulty in building a stable image acquisition system, and different types of instrument displays. To be specific, the changes in string length are reflected in the following aspects: (1) different number of characters in character string; (2) different character fonts and styles; (3) different character spacing and aspect ratios. Thus, more and more researchers focus on the problem of recognizing variable-length reading of multi-type digital instruments. Currently, the approaches of recognizing variable-length reading strings are mainly based on segmentation-based recognition methods by first splitting the string into individual characters, and then recognizing each character separately. [PERSON] et al. [12] split the characters separately by obtaining the equations of the lines on both sides of the character. However, this method was easily affected by position changes of characters, not suit for the application with muti-type instruments. Studies [13; 14] used vertical projection method to cut out the numbers according to the height-width ratio and other information of the image before recognition, which had the advantages of high recognition rate, high real-time performance and good reliability. Nevertheless, the image pre-processing algorithm is used for key operations, which may lead to segmentation failure if its effect is not good. [PERSON] et al. [15] directly located to each character in the string, thus avoiding obvious segmentation steps, but it required character-level annotation.