Feature Transformation with Generalized Learning Vector Quantization for Hand-Written Chinese Character Recognition
نویسنده
چکیده
In this paper, the generalized learning vector quantization (GLVQ) algorithm is applied to design a handwritten Chinese character recognition system. The system proposed herein consists of two modules, feature transformation and recognizer. The feature transformation module is designed to extract discriminative features to enhance the recognition performance. The initial feature transformation matrix is obtained by using Fisher’s linear discriminant (FLD) function. A template matching with minimum distance criterion recognizer is used and each character is represented by one reference template. These reference templates and the elements of the feature transformation matrix are trained by using the generalized learning vector quantization algorithm. In the experiments, 540100 (5401× 100) hand-written Chinese character samples are used to build the recognition system and the other 540100 (5401 × 100) samples are used to do the open test. A good performance of 92.18% accuracy is achieved by proposed system. key words: Handwritten Chinese character recognition, generalized learning vector quantization, Fisher’s linear discriminant, feature transformation
منابع مشابه
Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...
متن کاملHand-Written Chinese Character Recognizer
A n off-line hand-written Chinese character recognizer based on Contextual Vector Quantization (CVQ) supporting a vocabulary of 4,616 Chinese characters, alphanumerics and punctuation symbols has been reported. Trained with a sample for each character from each of 100 writers and tested on texts of 160,000 characters written b y another 200 writers, the average recognition rate is 77.2%. Two st...
متن کاملGeneralized Learning Vector Quantization
We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function . The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and ...
متن کاملAccuracy Improvement of Handwritten Character Recognition by Glvq
This paper deals with accuracy improvement of handwritten character recognition by the GLVQ (generalized learning vector quantization). In literature , the way of combining the FDA (Fisher discriminant analysis) and the GLVQ was investigated and evaluated to be effective for handwritten Chinese character recognition employing the minimum Euclidian distance classifier. In this paper, the project...
متن کاملHigh speed rough classification for handwritten characters using hierarchical learning vector quantization
Today , high accuracy of character recognition is attainable using Neural Network for problems with relatively small number of categories. But for large categories, like Chinese characters, it is difficult to reach the neural network convergence because of the “local minima problem” and a large number of calculation. Studies are being done t o solve the problem by splitting the neural network i...
متن کامل