Handwritten English Letters Recognition Based On LVQ Neural Network

With the development of society, English as an international common language has been increasingly widely used, so a large number of documents in English need to be tidied up , queried and statistic, and the English Document Recognition system can easily complete many Unimaginable work before. A complete English Document Recognition system is composed of documents segmentation, word segmentation, single-letter recognition, word recognition and lastly processing. And the recognition system for single letter is the core. The recognition of English document is usually based on words. And a small number of methods are depended on the object of the whole words, but most the single-character recognition, in other words you must gain the character after the segment of the word, and then recognize it.Handwriting Recognition system for English letters can be divided into three main modules, namely the image preprocessing, feature extraction and identify. In this paper, the main consideration is feature extraction and recognition. The mainly work of feature extraction is to extract structure and statistics characteristics, then integrate them, and get that is needed for the training and recognition work in the end. The artificial neural network identification methods are commonly used for the recognition work. In this paper, according to the characteristics of the handwriting letters I have chose the LVQ neural network, and made to their further improvement.In this paper, I use a research library of about 260,000 of a handwritten letters, and as required from training samples and test samples. Because the images of the letters library have not yet reached the feature extraction request, so I have to make appropriate pretreatment. First, the images are not the same size, and need to conduct a naturalization processing. Secondly it is necessary to do a Thinning Treatment and Contour Tracking work by the need to extract characteristics from their final images. In the procedure, I improve the algorithms, making the letter image better after pretreatment, help to extract better features, enhance recognition rate.After the pretreatment, extracting the features is need. The Projection features and Coarse Grid features are extracted form the original images while the Direction from the skeleton ones, and External from the Contour. And Direction features and Coarse Grid is a partial description of the features of the demographic characteristics, the Projection is characterized by overall description of the demographic characteristics, External Contour characteristic features of structural analysis. In this paper, these three characteristics are integrated in order to import to the neural network for training and recognition, and eventually in the experiment has reached a very good results.Finally, the neural network is trained to identify. In this article, 2,600 letters picture are randomly selected from the letters library as training samples and 1,040 other samples for testing. The samples are then imported to LVQ neural network for training and recognition, and compare to BP neural network.It is proved that, compared with BP network, simple structure, the LVQ neural network, just three-tier network, can achieved pattern recognition; and it does not need the input vector for normalization of orthogonal, only need to calculate the distance between the entering vector and competition layer so as to achieve pattern recognition; besides, the convergence rate is faster than the BP network, and the network does not exist the possibility of problem that BP may have a local minimum; in other words, LVQ neural network is not only simple, but also more efficient to identify. Therefore, the use of LVQ network for handwritten letter recognition is appropriate.