Novel Technique For Broadcast Footage Overlay Text Recognition

Living in a constant news cycle creates the need for automated tracking of events as they happen. This can be achieved through the investigation of broadcast overlay textual content. There exists a great amount of information to be deciphered via these means before further processing, with applications spanning from politics to sports. We utilize image processing to create mean cropping masks based on binary slice clustering from intelligent retrieval to identify areas of interest. This data is handed off to CEIR, based on the connectionist text proposal network (CTPN) to fine-tune the text locations and an advanced convolutional recurrent neural networks (CRNN) system to carry out text recognition to recognize the text strings. In order to improve the accuracy and reduce processing time, this novel approach utilizes a preprocessing mask identification and cropping module to reduce the amount of data being processed by the more finely tuned neural network.