Research On The Text Detection And Extraction From Complex Images

With the rapid development of multimedia technology and Internet technology, the number of digital images and video is growing at a tremendous rate. Embedded text in images and videos, to a certain extent describe and illustrate the content of images and videos, is an important expression of the image semantic content. If these words can be recognized automatically with computers, the understanding of the content of images and classifying images as well as other operations can be realized automatically with the assistant of computers. Based on this processing and with the help of matured means of text retrieval technology to retrieve the picture, a means for the application of content-based image and video etc. is providedThe researchers classify the images of text message to be extracted into three categories:document image, scene image and born-digital image. The existing optical character recognition software mainly deals with document images and produces good results. Although there are many methods having been proposed over the past years for text extraction and recognition, the text detection and extraction from complex images are still a challenge. Most researches have only studied relatively simple images or images with simple background. The research of text extraction from complex and low-resolution scene images and born-digital images is still in its initial stage.Automatic detecting and extracting text from complex images is a multi-step process, which comprises four stages:image binarization, CCs extraction, text-CCs textraction and characters recognization.In this paper, a new image binarization method is proposed. Based on the knowledge of the fine de-noising effect of wavelet, the complex images are firstly converted to gray images followed with the removal of the foreground text in the images as noises using wavelet decomposition, multi-wavelet filtering and wavelet reconstruction, and consequently get the background distribution of the image; Secondly the distribution of the foreground image is acquired from the differential operation on the grayscale image and the background distribution; Finally the binary images are obtained through the calculation of local threshold.Then connected component (CC) is calculated with the usage of wavelet based image binarization segmentation method and a series of CC are extracted from both non-text CC and text CC. the CRF model are built for the image, the features of the CC and the context are extracted, the training data sets and the testing data sets are achieved and the conditional probability model is built with the training data sets.This paper considers the problem of recognizing complex image character under tilt, uneven illumination, noise, edge softening and other different state conditions. We present SC-HOG combining sparse coding and gradient direction histogram to recognize abnormal characters.In this paper, we introduce STRHOG, an extended version of HOG, to the recognition of characters in complex images. Two more steps, Clipping and normalizing gradients matrices, are added in STRHOG to reduce the impact of scale and translation.With the above key technologies, this paper constructes a keyword-based sensitive network image filtering system. In this system the network administrator specifies the site to be detected, then the images of all the webpages in this website are taken out with web crawler technology and the digital image processing text extraction kits are called to process these images. Afterwards the extracted text information is fuzzy matched with the key words in the keyword library. When the matching exceeds the threshold, the image will be submitted to the network administrator for further treatment.