We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios.

翻译：我们提出结构化场景文本识别任务，要求场景文本OCR系统根据查询正则表达式在自然场景中识别文本。与通用场景文本OCR不同，结构化场景文本识别旨在根据用户提供的正则表达式动态调整场景文本检测与识别过程。为应对该任务，我们提出结构化文本识别器（STEP），该模型利用文本结构信息引导OCR流程。STEP能够处理包含空格的正则表达式，且不局限于词粒度的检测。我们的方法可在多种真实阅读场景中实现精准的零样本结构化文本识别，且仅使用公开数据集进行训练。为验证方法的有效性，我们引入了一个具有挑战性的新测试数据集，其中包含多种类型的词汇表外结构化文本，涵盖价格、日期、序列号、车牌等重要阅读应用场景。实验证明，STEP能够在所有测试场景中按需提供专业的OCR性能。