Research On Feature And Similarity Measurement In Character Recognition

Character is an important symbol of human civilization, as a heritage of human history and civilization of the important tool and media, It is to promote the role of human progress. Especially in the modern urban environment, the characters are almost everywhere. Newspapers, books, documents, reports, posters, cards and billboards, contains a large amount of characters, so automatic character detection and recognition technique in image search, office automation, human-computer interaction, robot navigation, unmanned aerial vehicles and other fields have broad application prospects. If the computer can automatically identify the characters in the image, it will provide a very natural communication between the computer and the communication mode.Experimental results show that the first recognition accuracy of the existing mature OCR recognition software in the large sample test set is 95%~97%,there is still room for improvement in the accuracy rate. Because the core of the recognition algorithm is the feature of text image extraction and similarity measure, the two important contents of text recognition are the feature engineering and similarity measure of image. The main step of character recognition is to extract the text image of the relevant features, and then based on a measure of the extracted features to measure. Since the extracted features and the measurement methods are not the same.so the accuracy of a variety of text recognition and efficiency is not the same.In view of the lack of the accuracy of the current character recognition. Based on statistical pattern recognition and structural pattern recognition, we propose an adaptive character recognition algorithm based on probability characteristics and structural features, through the different number of training samples to build the probability distribution matrix of Chinese characters in the measurement space. By comparing the similarity of the probability distribution matrix of the Chinese characters in the original image and the standard Chinese character library, the results of the classification of Chinese characters are achieved. The similarity measurement criteria is from point of view of the matrix of spatial structure and the probability of building. Experimental results show the algorithm in the training samples of the first recognition accuracy can reach 99.66%, in 1623 non training sample text image the first recognition correct rate reached 99.13%, 5515 non training sample text image in the first recognition correct rate can reach 98.57%. It can be concluded that the similarity measure method proposed in this paper is effective in character recognition.with the increasing of learning samples, this paper establishes the accuracy of the algorithm in the similarity measure standard and the space of progress could be have more adequate space for improvement.