## 1 Introduction
All of the above methods have explored solutions of recognizing variable-length reading strings to different degrees, and have achieved good results in their respective application scenarios. However, they are all based on a segmented thinking frame, whose generalization and robustness are extremely limited in real-industrial scenarios. Firstly, it is difficult to find a universal segmentation method due to different digital instrument display styles. Secondly, the error introduced by pre-processing and segmentation steps may directly reduce the final recognition accuracy. Finally, this recognition method is still limited to character-level classification, which requires a large amount of character-level annotation data and a huge workload. In recent years, the continuous development of deep learning has brought new vitality to digital instrument reading recognition. The alternative approaches are based on the segmentation-free idea in which the string is recognized without having to be previously split into isolated characters. [PERSON] et al. [16] proposed a segmentation-free recognition algorithm for handwritten digit strings of unknown length based on dynamic selection of classifiers, including a length classifier and other three string classifiers (10 [0... 9], 100 [00... 99], and 1000 [000... 999]). However, for the digital instrument reading recognition task, the existence of special characters and the increase in the number of characters will greatly increase the number of the classifiers (much larger than 1110 classes), leading to an increased training burden. In 2018, [PERSON] [17] proposed a digital instrument reading recognition algorithm based on a full convolution network, which directly obtained the result of variable-length string through graph to graph prediction, but additional post-processing algorithms were needed to extract variable-length reading strings from the prediction matrix, resulting in complex network structure, which is not conducive to practical application. In order to solve this problem more economically and effectively, we expect the prediction results of deep neural networks to combine both class information and location information of characters, and finally achieve pixel-levelprediction of images, thus eliminating complex segmentation strategies and reducing computational burden at the same time. In the industrial scenario with multi-type digital instruments, a string in a digital instrument image can be regarded as an ordered string of characters with variable styles and special small characters, therefore, we consider trying the sequence-based segmentation-free method. Considering that the recurrent neural network (RNN) [18] are very good at handling sequence data and the connectionist temporal classification (CTC) [19] can solve the problem of one-dimensional sequence alignment without character-level annotations, the RNN-CTC framework is particularly suitable for the task of sequence labels with explicit order information, such as license plate recognition [20; 21], CAPTCHA recognition [22; 23] and handwritten digit recognition [24]. However, although CTC is an effective decoding strategy, it also has some limitations in the reading recognition task of multi-type digital instruments: (1) It needs high requirements for feature extraction. Especially for characters with variable styles or some small characters such as \"-\" \"-\", CTC will output \"null\" for sequences that cannot be recognized, however, its alignment rules of de-duplication and de-blank will easily cause wrong decoding. (2) The short-distance dependencies are important. Since CTC decoding allows repeated characters and blank labels, a label, especially a wide character, can be jointly predicted by more than one sequences. Some neighboring sequences have complementary features and there is some correlation within the feature sequences that need to be captured effectively.