Bor-Shenn Jeng

Optimal segmentation of handwritten Chinese signatures using wavelet transforms

We propose a new segmenting method for handwritten Chi- nese signatures based on the wavelet transform for signature verifica- tion. There are some differences in identifying a handwritten signature and in recognizing a handwritten character because there are meaning- ful features hidden in writing habits when an individual is signing his or her signature. These features exhibit themselves in the pen-down, pen- up, and in the corner of a stroke. Therefore the segmentation for identi- fying a signature and for recognizing a character should be different even though the same characters are involved. We propose to segment an input signature curve at the inflection points, and we locate the inflec- tion points by detecting the zero-crossing points of the wavelet trans- forms of the input signature. Experimental results show that this new segmenting method has better segmentation capability than other meth- ods that are usually used.

Optical Chinese Character Recognition Using Accumulated Stroke Features

An intelligent optical Chinese character recognition system using accumulated stroke features has been developed to solve the input problem of Chinese characters. The hardware architecture of the system is built on an IBM PC-AT with three extension boards: the preprocessor board, the feature extraction board, and the matching recognition board. The system can recognize, at the same time in the same program, either printed or handwritten Chinese characters of different styles and sizes. At present, a total of 5401 commonly used Chinese characters can be recognized. Results show that 99% of printed characters and 90% of constrained handwritten characters can be correctly recognized, at a speed of about 300 characters per minute.

On-line Chinese signature verification with mixture of experts

We propose an on-line Chinese signature verification system based on mixture of experts to further improve the reliability of a signature verification system. As we know, the signature of a certain signer has some personal particular characteristics. We collect these characteristics, and classify them into several feature vectors. In our system, an individual feature vector corresponds to an expert which performs the function of signature verification. According to the particular habit of an individual signer, an individual expert with a particular feature vector plays a different role with different power to a different signer. These verification results should be weighted by a weighted controller. The function of the weighted controller works as the personal habit function. In our proposed system, there are seven verification experts with different feature vectors to make their verification decision. Based on these results, the combination unit with a weighted controller makes the final decision. The weighted controller controls the weight of the decision between the experts and the individual signer. Then, the combination of mixture results of the seven experts is attained, and a signature verification system with a personal-oriented feature decision is approached. Finally, the performance of our system is demonstrated in several experiments which show a high success rate of verification.

Handwritten Chinese signature verification based on attributed string matching of stroke linkage order

We propose an attributed string matching approach based on the writing sequences of an input signature for Chinese signature verification. It is impossible to find features of a signature that are invariant with respect to individual writing style. The aim of our research is to find a particular feature set that will exhibit small intraclass variance. A signer tends to connect consecutive strokes in a constant sequences when signing a signature, especially for Chinese signatures. Therefore, the writing sequences (stroke order) of a signature can be regarded as the personal signature feature. In order to obtain an attributed string that will be used in the string matching similarity calculation, we must split an input signature into several segments from the corners of strokes at first. Since the wavelet transform has been used in the field of edge detection and corner detection for a long time, we can use it as a tool for corner detection to find an optimal segmentation set. Therefore, an attributed string for consecutive segments can be calculated after the signature has been split into several segments suitably by means of a wavelet transform. The elements in the attributed vector include relative angle of adjoining segments, the direction code of the segment, writing duration time of the segment, and the length of the segment. The total relative angle can also be summarized over all of the relative angles of segments to result in another individual feature. This total relative angle can be used to filter the rougher forgery signatures. Our attributed string matching method is also based on the extraction of irreducible characteristic points. The experimental results show a very excellent discrimination capability.

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%.

Optical Chinese character recognition system using a new pipelined matching and sorting very large scale integration

A VLSI implementation of the optical Chinese character recognition (OCCR) system with pipelined and parallel structure is presented. We also propose an efficient method for performing block classification and character segmentation as well as an effective and adaptive feature extraction algorithm for recognizing multifont printed Chinese characters. With the complex and huge amount of data involved in Chinese characters, their recognition requires numerous complex computations. Therefore, to improve the recognition efficiency for practical applications, a VLSI chip is designed and fabricated. To preserve a certain degree of flexibility so that various recognition algorithms can be implemented with the system, only the most time-consumig parts are implemented into the VLSI circuit. By combining the VLSI technology and the effective Chinese character recognition algorithm, a practical OCCR system with high speed, high-recognition rate, and accumulated learning capability is developed. Based on the experimental results, the VLSI chip can process up to 200 characters/s, which is one hundred times faster than the original software algorithm. The recognition rates of three different test conditions are also given.

A new approach to on-line Chinese signature verification using multiple experts

Summary form only given. In this paper, we present a system for online Chinese signature verification which is based on the combination of nine verification experts. First, we briefly describe the individual verification experts. Then, a method for expert combination is discussed. Finally, the performance of the combination is demonstrated in several practical experiments which show a high success rate of verification.

Automated entry system for Chinese printed documents

Abstract In this paper, we present a new automated Chinese printed document entry system. This system features automated text/ graph segmentation, and multi-font, multi-size printed Chinese character recognition. Experimental results show that 95.8–99.4% of the top 10 printed characters can be correctly recognized, with the speed of 0.16 seconds/character.

Intelligent-based optical Chinese character recognition system by using integrated character segmentation and text compression techniques

This paper presents a method dealing with mixed character segmentation. Conventionally, the region-based approach has been proposed to process the mixed character segmentation , but failure may occur due to some English lettters’ block size which is similar to that of Chinese characters. And also some Chinese characters which are composed of two or three parts will be recognized as two or three English letters. In this paper, we propose an intelligent method to overcome this difficulty. It is the integrated region-based and recognition-based approach. Furthermore, we assume the internal code of character to be the source symbol for coding. So if there are some characters which appear in the text repeatedly, then we will remove the redundancy by compressing text according to the algorithm of Lempel-Ziv coding.

Processing Chinese form-document by using optical character recognition

We propose a document analysis system to separate the graphic image and text and optical Chinese character recognition to recognize characters for processing Chinese form-document. It is a useful and efficient tool. 1.