FASTex: Efficient Unconstrained Scene Text Detector

Observing that text in virtually any script is formed of strokes, we propose a novel easy-to-implement stroke detector which is significantly faster and produces significantly less false detections than the detectors commonly used in scene text localization. First, stroke-specific keypoints are efficiently detected. Text fragments are subsequently extracted by local thresholding guided by keypoint properties. Classification based on effectively calculated features eliminates non-text segmentations. The stroke-specific keypoints produce 2 times less segmentations and still detects 25% more characters than the commonly exploited MSER detector and the process is 4 times faster. After a novel efficient classification step, the number of segmentations is reduced to 7 times less than the standard method and is still almost 3 times faster. All stages of the proposed pipeline are scaleand rotationinvariant and support a wide variety of scripts (Latin, Hebrew, Chinese, etc.) and fonts. When the proposed detector is plugged into a scene text localization and recognition pipeline, a state-of-the-art text localization accuracy is maintained whilst the processing time is significantly reduced.