Study On Key Technologies Of Scene Text Recognition

Images in natural scene always contain rich text information, and they can help people to capture and understand the content and meaning of natural scene image to a large extend. So text in natural scene plays an important role in the image visual information acquisition. If humans can use computer to recognize the text content in natural scene image automatically, and apply it to auxiliary blind navigation, unmanned navigation, security, crisis prevention and treatment and other fields, our life will become more convenient.Scene text recognition and the traditional optical character recognition (OCR) have essential difference, which mainly lies in scene text is mainly obtained by digital camera or video camera, so the image has color not consistent, brightness uneven, background complicated and other strong noise, so text in the image may be deformed, low resolution, strokes fracture and other issues. These factors bring scene text recognition a lot of difficulties, and there are many problems facing challenges. In this paper, text in natural scene recognition system is studied, and the research on the key technical issues is carried on.According to the problem that the background of scene text is complex and it will affect the text recognition, the characteristic of scene text image and research situation of text segmentation are analyzed, and improved text image segmentation method based on spectral clustering is brought out on the basis of decision to do image preprocessing as the first step to separate the text from the complex background. Firstly the similarity function is established considering the characteristic of the text image. According to the color distribution of scene images the color space is quantized to get limited number pixel sets of each kind of color using color histogram, and the affinity matrix is constructed under the quantized levels. Finally, the method uses the spectral clustering under Ncut criterion to segment images. The method uses color sets quantized as vertex of graph to simplify the weighted graph model so the computational complexity of spectral clustering is reduced significantly and the application ability of the spectral clustering method in image segmentation is improved. Experiments on the test images of ICDAR2003,2009competition and plenty of other text images have been done, and the results show that the proposed method is with good performance on text segmentation.An effective perspective distortion correction method is presented to resolve the perspective distortion correction in scene text recognition caused by the tilt of text carrier itself or camera view. In this paper mathematic morphology is employed to select morphological factors for various distortion; then the clustering information is extracted by using clustering method and nearest neighbor method to fit text base-line and followed by some statistic calculation such as RANSAC (Random Sample Consensus) to locate the base-line so as to extract the distortion parameters. At last affine transformation is applied to finish the distortion correction for text images. Experiments show the method in this paper is effective to correct the text image distortion, and improve the text recognition rate significantly. Especially for scene text with a few lines, this method has advantages.According to the problems of scene text recognition such as lower resolution, poor quality, serious noise and others, this paper uses Gabor wavelet transform feature with high robustness as classification feature. And further the original Gabor wavelet transform is improved by pre-classification of direction and feature fusion combined with histogram. Series of comparative experiments prove that the proposed features have good classification ability for low quality character image with fuzzy strokes.A text recognition method based on CCA of background is proposed in order to seek a high-performance scene text recognition system. According to the correlationship between scene text and background the method extracts the CCA feature as the classification feature for character to mine correlation between background text using CCA. The method obtained a satisfied result and the experimental data show CCA feature may significantly improves the performance of scene text recognition and the method is effective. This method breaks through the limitations of traditional recognition ways which onlv consider the characteristic of text itself, and makes full use of the surrounding information of the text in image. That is a new breakthrough of scene text recognition. The experimental results also show that using background information to assist recognition is a worthy subject for further research. It provides a new research idea to achieve high-performance scene text recognition system.