  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.
