{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:01:32Z","timestamp":1760148092786,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51275325"],"award-info":[{"award-number":["No. 51275325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and handwritten digit recognition, the recognition task of multi-type digital instruments faces greater challenges due to the reading strings are variable-length with different fonts, different spacing and aspect ratios. In order to overcome this shortcoming, we propose a novel short-memory sequence-based model for variable-length reading recognition. First, we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor for capturing effective features from characters with different fonts of multi-type digital instruments images. Then, we apply an RNN-based sequence module, which strengthens short-distance dependencies while reducing the long-distance trending memory of the reading string, to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. Experimental results show that this method is effective on variable-length instrument reading recognition task, especially for invisible data, which proves that our method has outstanding generalization and robustness in real-industrial applications.<\/jats:p>","DOI":"10.3390\/a16040192","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T10:19:33Z","timestamp":1680257973000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Short-Memory Sequence-Based Model for Variable-Length Reading Recognition of Multi-Type Digital Instruments in Industrial Scenarios"],"prefix":"10.3390","volume":"16","author":[{"given":"Shenghan","family":"Wei","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610041, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"National Institute of Measurement and Testing Technology, Chengdu 610021, China"}]},{"given":"Yong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610041, China"},{"name":"National Institute of Measurement and Testing Technology, Chengdu 610021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4652-8009","authenticated-orcid":false,"given":"Suixian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610041, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.17706\/IJCCE.2020.9.4.185-192","article-title":"Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network","volume":"9","author":"Shan","year":"2020","journal-title":"Int. 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