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：本文提出了结构化场景文本识别任务，要求场景文本OCR系统能够根据用户提供的查询正则表达式，在自然场景中完成文本定位。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定义的正则表达式动态调整场景文本检测与识别过程。针对该任务，我们提出了结构化文本定位器（STEP），该模型利用提供的文本结构引导OCR流程。STEP能够处理包含空格的正则表达式，且不受限于单词级粒度的检测。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，且仅使用公开数据进行训练。为验证方法的有效性，我们引入了一个包含多种类型词汇外结构化文本的新颖测试数据集，这些文本涵盖了价格、日期、序列号、车牌等重要阅读应用领域。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。