Sentence examples for scene text from inspiring English sources

Though different scene text recognition techniques have been developed, scene text recognition under a generic condition is still a very open and challenging research problem.

For the focused scene text images the non-text candidates are filtered via an FIS.

First it converts each word image into a sequential signal for the scene text recognition.

A novel natural scene text detection method is proposed in this paper.

In this paper, we present a robust system to detect natural scene text according to text region appearances.

In this paper, we proposed a novel scene text recognition technique that performs word level recognition without character segmentation.

Experiments show that the proposed techniques provide superior scene character recognition accuracy and are capable of recognizing scene texts of different scripts and languages.

In this paper a framework is proposed to localize both Farsi/Arabic and Latin scene texts with different sizes, fonts and orientations.

Third, by integrating multiple RNNs, an accurate recognition system is developed which is capable of recognizing scene texts including those heavily touched ones without character segmentation.

In this paper, we extend the Histogram of Oriented Gradient (HOG) and propose two new feature descriptors: Co-occurrence HOG (Co-HOG) and Convolutional Co-HOG (ConvCo-HOG) for accurate recognition of scene texts of different languages.

These three images - Private Jet scene, "Widen" text scene and a Ronchi ruling with a frequency of 0.0148 lp/mm - were displayed on a monitor above the Stretchcam prototype.