On-line Chinese Character Recognition System Research Articles

Candidate Selection in On-Line Chinese Character Recognition System Using Voting Scheme

In this paper, a candidate selection method using a voting scheme is proposed for speeding up a on-line Chinese character recognition system. Three steps in this method are described as follows: First, several simple features are extracted from the input ink data, such as peripheral code. Then, the number of votes which denote the coarse matching scores between input features and 5401 reference features of Chinese characters are computed. Finally, the templates of Chinese characters whose votes are higher than a prescribed threshold are selected to perform detailed matching. Some experimental results show that the proposed selection method is a suitable tool for speeding up a character recognition system under the condition of maintaining the recognition rate.

On-Line Chinese Character Recognition Via a Representation of Spatial Relationships Between Strokes

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

Intelligent radical‐based on‐line Chinese character recognition system

An intelligent radical-based on-line Chinese character recognition (OLCCR) system is proposed. We make use of the characteristic that most Chinese characters possess radicals and develop a radicalbased preclassification technique. In the procedure of recognizing radicals, potential candidate radicals are identified from an input character first and then matched with radical templates. Based on the matched radicals, corresponding candidate characters with a smaller matching distance are thus identified. Postprocessing, which utilizes the information of the first and last stable line segments, is incorporated to improve the recognition accuracy. Experiments are carried out to evaluate the performance of the proposed OLCCR system and the average recognition rate is improved from 96.06 to 96.52% if only the first winner is selected and the average recognition time is shortened by about 44%.

On-line handwritten Chinese character recognition via a fuzzy attribute representation

Chinese characters are constructed by strokes according to structural rules. Therefore, the geometric configurations of characters are important features for character recognition. In handwritten characters, stroke shapes and their spatial relations may vary to some extent. The attribute value of a structural identification is then a fuzzy quantity rather than a binary quantity. Recognizing these facts, we propose a fuzzy attribute representation (FAR) to describe the structural features of handwritten Chinese characters for an on-line Chinese character recognition (OLCCR) system. With a FAR. a fuzzy attribute graph for each handwritten character is created, and the character recognition process is thus transformed into a simple graph matching problem. This character representation and our proposed recognition method allow us to relax the constraints on stroke order and stroke connection. The graph model provides a generalized character representation that can easily incorporate newly added characters into an OLCCR system with an automatic learning capability. The fuzzy representation can describe the degree of structural deformation in handwritten characters. The character matching algorithm is designed to tolerate structural deformations to some extent. Therefore, even input characters with deformations can be recognized correctly once the reference dictionary of the recognition system has been trained using a few representative learning samples. Experimental results are provided to show the effectiveness of the proposed method.

A neural network-based on-line Chinese character recognition system

A neural network-based on-line Chinese character recognition (OLCCR) system is presented. In this paper, a back-propagation neural network model is proposed for solving the pattern-matching problems in OLCCR, instead of those non-neural network-based algorithms. This OLCCR system will enable us to recognize handwritten Chinese characters in real time. Each recognition process includes two main phases: (A) the feature-extraction phase, in which a feature vector based on reference stroke information can be extracted, and (B) the character-matching phase, in which the feature vector is matched with a group of standard Chinese reference character feature vectors. Experimental results show that it takes about 0.25 s to recognize a handwritten character and that a recognition rate of about 91% can be achieved.