Abstract
Detecting and recognizing text in natural scenes (e.g. streets, restaurants, shops, etc.) could be a part of an artificial intelligence system, especially with regard to the speech synthesis system. Properly detected text is passed to a recognition stage and then to the speech synthesis system, which translates text to speech. Research is carried out for the ‘Toucan Eye’ project — embedded device with artificial intelligence system able to help people with impaired sight. Due to constrained resources and abilities of embedded devices, criteria for text spotting must be met. First criterion is quality of detected and recognized regions with text and the second is time spent on both operations. Particular stages of the system and chosen methods of text spotting under aforementioned constraints are presented.