Actually there are still quite big issues in processing this kind of images. In this case the problem has not been solved at all. For example a photo taken by a twitter user and then posted on the social platform. The image are gray scale, very good contrast, no specific issue in single character contour detection and little problems due to lighting or shadows.Ī completely different scenario starts being depicted when we deal with natural scenes. There are also very good open source tools out there, such as Tesseract-OCR, which can read and detect up to 60 languages. It is pretty common practice to scan a sheet of paper and use some standard software to convert it to a text file. Actually, at present, the problem of character recognition from black and white documents is considered solved. This field has been object of very intensive study in the past decades. The topic I was interested to dive into is OCR which stands for Optical Character Recognition. One can cite other sophisticated applications such as animal species or plants identification, human beings detection or, more in general, extraction of any kind of information of commercial use.

Automatic text detection and character recognition is just an example. Machine learning obviously plays a very significant role in this field. Thus the increased interest of the industry towards this kind of problems is completely justified. Especially with the growing market of smart phones people has started producing a huge amount of photos and videos which are continuously streamed on social platforms. The importance of image processing has increased a lot during the last years. In this post I’m going to summarize the work I’ve done on Text Recognition in Natural Scenes as part of my second portfolio project at Data Science Retreat.

Ipad text recognition code