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<p>Structured Query Language (SQL) is a standard language for interacting with relational databases and is widely used across various information systems, either through direct query execution or via object-relational mapping (ORM) frameworks. Recent approaches have focused on converting natural language into SQL to simplify database development for users without programming expertise. However, these methods overlook direct translation from sign language—an essential modality for users such as the deaf community who may lack experience with SQL syntax. In this paper, we present <i>SIGN2SQL</i>, an innovative end-to-end framework that generates SQL queries from signed input. The system first employs a dedicated gesture recognition module to interpret the visual signals, followed by a convolutional neural network (CNN)-based model that produces the corresponding SQL statements. Trained on a well-annotated dataset, SIGN2SQL is evaluated against multiple pipeline-based baselines. Experimental results demonstrate that SIGN2SQL outperforms existing methods in both effectiveness and efficiency, particularly for SELECT statements with WHERE clauses. It achieves an execution accuracy of 89.8%, highlighting its potential as an accessible and inclusive database interaction interface.</p>

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A sign language to SQL query translation system for enhancing database accessibility

  • 期刊论文

  • 发表日期：

    2025年10月7日

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  • Guocang Yang,
  • Dawei Yuan,
  • Tao Zhang,
  • Zhenghan Chen

摘要

Structured Query Language (SQL) is a standard language for interacting with relational databases and is widely used across various information systems, either through direct query execution or via object-relational mapping (ORM) frameworks. Recent approaches have focused on converting natural language into SQL to simplify database development for users without programming expertise. However, these methods overlook direct translation from sign language—an essential modality for users such as the deaf community who may lack experience with SQL syntax. In this paper, we present SIGN2SQL, an innovative end-to-end framework that generates SQL queries from signed input. The system first employs a dedicated gesture recognition module to interpret the visual signals, followed by a convolutional neural network (CNN)-based model that produces the corresponding SQL statements. Trained on a well-annotated dataset, SIGN2SQL is evaluated against multiple pipeline-based baselines. Experimental results demonstrate that SIGN2SQL outperforms existing methods in both effectiveness and efficiency, particularly for SELECT statements with WHERE clauses. It achieves an execution accuracy of 89.8%, highlighting its potential as an accessible and inclusive database interaction interface.