Abstract: Due to the complex character strokes,the quality of video-extracted Chinese character images is often poor,for which traditional optical character recognition(OCR) could not get desired results.To address this problem,this paper presents a two-stage scheme for low-quality Chinese character recognition based on block search.The block structure of the Chinese character image is built,along with a training set,imitating low-quality Chinese characters.And an index is generated after extracting the features from each block of the training set by applying principle component analysis.This scheme retrieves candidate character set from the index by block search and voting(1st stage),and then recognizes the character according to the salience of voting result assisted with global structural feature match(2nd stage).The experimental results have demonstrated that this scheme has better recognition rate for low-quality Chinese character images compared with traditional OCR method.