Research On Arbitrary Orientation And Scale Text Detection Algorithm Based On Deep Learning

As a symbol of human civilization,and the carrier of information exchange,text widely exists in images of natural scenes(License plate,Road sign,etc.),compared with the content of other natural scenes,Natural scene text has stronger logic and more general expression.Accurately detecting text in images is crucial to the analysis and understanding of scene content.Previous scene text detection methods have progressed substantially over the past years.However,large geometry(e.g.,orientation)variances are the key challenges in the scene text detection.In this work,we first conduct experiments to investigate the capacity of networks for learning geometry variances on detecting scene texts,and find that networks can handle only limited text geometry variances.Then,we put forward a novel Geometry Normalization Module(GNM)with multiple branches,each of which is composed of one Scale Normalization Unit and one Orientation Normalization Unit,to normalize each text instance to one desired canonical geometry range through at least one branch.The GNM is general and readily plugged into existing convolutional neural network based text detectors to construct end-to-end Geometry Normalization Networks(GNNets).Moreover,we propose a geometry-aware training scheme to effectively train the GNNets by sampling and augmenting text instances from a uniform geometry variance distribution.Finally,experiments on popular benchmarks of ICDAR 2015 and ICDAR 2017 MLT validate that our method outperforms all the state-of-the-art approaches remarkably by obtaining one-forward test F-scores of 88.52 and 74.54 respectively.