On-line Chinese Character Research Articles

Confusion set recognition of on-line Chinese characters by artificial intelligence technique

In this paper, a knowledge based on-line recognition system for the square written Chinese similar characters is presented by utilizing Chinese character knowledge and artificial intelligence techniques. The reference patterns are represented by the heuristic knowledge describing them. The knowledge consists of the knowledge primitives designed in the system. For the pattern pair matching made on an unknown pattern, the matching recognition is made by utilizing problem reduction strategy, goal-driven inferencing like matching, and AND-OR tree searching algorithm. The method is developed and concatenated to a first on-line recognition system specially for recognizing similar characters. The recognized similar characters to be researched in the experimental system are chosen from the part of the error recognition results of the first system, totally 96 similar characters. The average recognition rate of 99.9% can be achieved. The results suggest that the knowledge can present efficient distinguishing ability and the method is reasonable and feasible.

Bipartite weighted matching for on-line handwritten Chinese character recognition

The matching of line segments between input and prototype characters can be formulated as bipartite weighted matching problem. Under the assumption that the distance of the two line segments and the unmatched penalty of any line segment are given, the matching goal is to find a matching such that the sum of the weights of matching edges and the penalties of unmatched vertices is minimum. In this paper, the Hungarian method is applied to solve the matching problem by a reduction algorithm. Moreover, a greedy algorithm based on the Hungarian method is proposed by restricting the above matching which satisfies the constraints of geometric relation. For each iteration in the greedy algorithm, a matched pair is deleted if the relation of their neighbors does not match and a new matching is then found by applying Hungarian method. Finally, we can find a stable matching that preserves the geometric relation. We have implemented this method to recognize on-line Chinese handwritten characters permitting both stroke-order variation and stroke-number variation and a 91% recognition rate is attained.

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.

Coarse classification of on-line Chinese characters via structure feature-based method

In this paper, we will propose a structure feature-based coarse classification mechanism to classify the on-line handwritten Chinese characters. The main purpose of coarse classification is to classify on-line Chinese characters into one of the six structure types and divide them into separated parts accordingly, if they can be divided. Our proposed coarse classification mechanism can be used to classify the following three writing style categories: square writing characters, partial connected characters, and script characters. Two structure classification methods for coarse classification are proposed in this paper. One is the line segment order method, and another is the projection method. Fourteen databases of 5400 frequently used Chinese characters are tested in our experiment. The average classification rates of square writing characters, partial connected characters, and full connected characters are 96.7%, 92.8%, and 91.7%, respectively. The experimental results reveal the feasibility and promise of our proposed approach.

Knowledge model based approach in recognition of on-line Chinese characters

A knowledge model-based OCR system is presented for the recognition of on-line connected stroke Chinese characters. In the approach, segment attributes are first extracted to characterize the segment sequence of an unknown character. Next, radical recognition based on model matching is adopted as the coarse classification to reduce the number of candidate characters before detailed matching. Finally, a deviation modeling method is proposed to recognize not only regular writing characters but also characters with stroke-order and stroke-number deviations. The effectiveness of the approach is verified by experiments on the recognition of on-line Chinese characters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Attributed string matching by split-and-merge for on-line Chinese character recognition

Consecutive strokes of Chinese characters tend to be connected in fast writing, and this causes a problem for most stroke-based recognition approaches. A recognition scheme for recognizing cursive Chinese characters under the constraint of correct stroke writing orders is proposed. The recognition scheme consists of two phases: candidate character selection and detailed matching. In the former phase, an input script with N strokes is used to split the strokes of each reference character into N corresponding parts. In the latter phase, the connected input strokes are broken into multiple strokes under the guidance of candidate characters. In both phases, dynamic programming is employed for stroke or character matching. Good experimental results prove the feasibility of the proposed approach for cursive Chinese character recognition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Approaches to chinese character recognition

Work on the recognition of Chinese characters is reviewed. All of this work has been reported in just the last nine years, and most of it since 1970. The topics covered are: printed Chinese Character Recognition—Half a dozen groups have tackled this problem, using methods as varied as syntactic description, projection profiles, template matching. Fourier transforms, and hierarchical processing: handwritten Chinese Character Recognition. This is a somewhat more difficult problem. The approaches tried include a stroke generation technique and a statistical Markov chain analysis; on-line Recognition—Several investigators have reported on work in on-line Chinese character recognition. Input for on-line recognition is dynamic, using a suitable device such as a tablet. Because the strokes are isolated in time and their sequence known, this is a substantially easier recognition task: on-line Analysis—Less ambitious than on-line recognition is the on-line analysis of Chinese characters falling short of recognition. Several useful applications have been reported, including CAI and dictionary-lookup. The paper is intended as a companion to a paper (6) on formal models of the structure of Chinese characters and their application in computerized systems.