A Syntactic Omni-Font Character Recognition System

The author introduces a syntactic omni-font character recognition system that recognizes a wide range of fonts, including handprinted characters. A structural pattern-matching approach is used. Essentially, a set of loosely constrained rules specify pattern components and their interrelationships. The robustness of the system is derived from the orthogonal set of pattern descriptors, location functions, and the manner in which they are combined to exploit the topological structure of characters. By virtue of the new pattern description language, PDL, the user may easily write rules to define new patterns for the system to recognize. The system also features scale-invariance and user-definable sensitivity to tilt orientation. The system has achieved a 95. 2% recognition rate

Online Japanese Character Recognition Using Trajectory-Based Normalization and Direction Feature Extraction

http://www.suvisoft.comThis paper describes an online Japanese character recognition system using advanced techniques of pattern normalization and direction feature extraction. The normalization of point coordinates and the decomposition of direction elements are directly performed on online trajectory, and therefore, are computationally efficient. We compare one-dimensional and pseudo two-dimensional (pseudo 2D) normalization methods, as well as direction features from original pattern and from normalized pattern. In experiments on the TUAT HANDS databases, the pseudo 2D normalization methods yielded superior performance, while direction features from original pattern and from normalized pattern made little difference

A scheme of on-line Chinese character recognition using neural networks

[[abstract]]The paper proposes a scheme of online Chinese character recognition, based on neural networks. The supervised backpropagation algorithm is used to train the network. The input character is converted as a sequence of virtual stroke segments as well as real stroke segments, which is a good feature exactly describing the complete structure of a character, and is to be extracted by our system. In order to simplify the recognition process and reduce the recognition time, the neural network is divided into several subnetworks. Each of them is responsible for recognizing a group of about 75 character patterns. In other words, the huge set of Chinese characters is divided into several groups according to the numbers of stroke segments in the characters, and for each group of characters, a specific subnetwork is trained in order to recognize every character in the group. Whenever the system accepts an input Chinese character, it will calculate the number of stroke segments, including virtual stroke segments as well as real stroke segments in that character, and then determine which subnets to enter for recognition process. The system is allowed to accept and recognize some interconnected characters. The algorithm was experimentally implemented in a personal computer system, it accepts interconnected Chinese characters written on an electronic tablet, and performs recognition in real time. Our experiment showed that recognition accuracy exceeded 96% on the test example.[[conferencetype]]國際[[conferencedate]]19971012~19971015[[booktype]]紙本[[conferencelocation]]Orlando, FL, US

Consistent relaxation matching for handwritten Chinese character recognition

Due to the complexity in structure and the various distortions (translation, rotation, shifting, and deformation) in different writing styles of Handwritten Chinese Characters(HCCs), it is more suitable to use a structural matching algorithm for computer recognition of HCC. Relaxation matching is a powerful technique which can tolerate considerable distortion. However, most relaxation techniques so far developed for Handwritten Chinese Character Recognition (HCCR) are based on a probabilistic relaxation scheme. In this paper, based on local constraint of relaxation labelling and optimization theory, we apply a new relaxation matching technique to handwritten character recognition. From the properties of the compatibility constraints, several rules are devised to guide the design of the compatibility function, which plays an important role in the relaxation process. By parallel use of local contextual information of geometric relaxationship among strokes of two characters, the ambiguity between them can be relaxed iteratively to achieve optimal consistent matching.published_or_final_versio