Character Recognition Method Research Articles

3次元テンプレートマッチングによる指文字認識

This paper proposes a method for Japanese finger character recognition, using a 3-dimensional (3D) scanner. A hand is a complex dexterous manipulator, evolved to be more complex than any other animals. The hand, being capable of making many different complex shapes, it is ideal for communicating using gestures. The recognition of a whole language, such as the Japanese finger characters, requires the differentiation of subtle similar positioning of each digit. To know the exact 3D position of the hand's digits and overall shape, data gloves had been developed, but these are inconvenient to use. 2D image recognition systems struggle with recreating the 3D information. To capture the 3D information, the proposed method uses a 3D scanner, and then makes matches with 3D templates representing each unique character. Experimental results show that the proposed method recognizes a greater number of characters than existing 2D-based systems with recognition accuracy, on average of 93% for 9 testees, and a peak of over 98% for 4 of them.

Improvements in Real-Time Feature-Based License Plate Character Recognition

This paper presents an effective method for segmenting and recognizing license plate characters on a moving vehicle in actual outdoor environment. It is applicable to various camera conditions and installation environment and provides some effective methods to improve the rate of recognition and procedure speed. This paper is concerned not only implementations in laboratory environment but also in outdoor environment like roads, entrances of parking areas and other similar places where license recognition is needed. The digital images used can be obtained from any digital camera. The proposed vehicle license plate character recognition methods are constructed from a feature-based approach. By regulating a character, a unique structural feature for each character can be extracted. The extracted feature is insensible to changes in the outdoor environments. The proposed recognition system has two main processes: 1) to construct the feature information and 2) to segment a character image from any object image of a license plate and identify it. The relative regulations, the similarity and dissimilarity between each character, are used as recognition features.

Robust feature extraction method based on run‐length compensation for degraded character recognition

AbstractConventional features are robust for recognizing either deformed or degraded characters. Most conventional character recognition methods address either one or the other. This paper proposes a feature extraction method that is robust for both of them. Run‐length compensation is introduced for approximately extracting directional run‐lengths of strokes from degraded handwritten characters. This technique is applied to the conventional feature vector based on directional run‐lengths. Experiments for handwritten characters with additive or subtractive noise show that the proposed feature is superior to conventional ones over a wide range of the degree of noise. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(9): 1–9, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10643

A segmentation-free approach to recognise printed Sinhala script using linear symmetry

In this paper, a novel approach for printed character recognition using linear symmetry is proposed. When the conventional character recognition methods such as the artificial neural network based techniques are used to recognise Brahmi Sinhala script, segmentation of modified characters into modifier symbols and basic characters is a necessity but a complex issue. The large size of the character set makes the whole recognition process even more complex. In contrast, in the proposed method, the orientation features are effectively used to recognise characters directly using a standard alphabet as the basis without the need for segmentation into basic components. The edge detection algorithm using linear symmetry recognises vertical modifiers. The linear symmetry principle is also used to determine the skew angle. Experiments with the aim for an optical character recognition system for the printed Sinhala script show favourable results.

Electronic Patient Registration and Tracking at Mass Vaccination Clinics

To protect the citizens of the United States from the use of dangerous biological agents, the Center for Disease Control and Prevention (CDC) has been actively preparing to deal with the consequences of such an attack. Their plans include the deployment of mass immunization clinics to handle postevent vaccinations. As part of the planning efforts by the Western New York Public Health Alliance, a Web-based electronic patient registration and tracking system was developed and tested at a recent trial smallpox vaccination clinic. Initial goals were to determine what the pitfalls and benefits of using such a system might be in comparison to other methods of data collection. This exercise proved that use of an electronic system capable of scanning two-dimensional bar codes was superior to both paper-based and optical character recognition (OCR) methods of data collection and management. Major improvements in speed and/or accuracy were evident in all areas of the clinic, especially in patient registration, vaccine tracking and postclinic data analysis.

A confidence value estimation method for handwritten Kanji character recognition and its application to candidate reduction

This paper describes a method for estimating a confidence value (CV) by which we can express the potential correctness of handwritten Kanji character recognition candidates. An accumulated confidence value (ACV), calculated as the sum of CVs, is also applied to reduce the number of candidates. Such reduction is vital to increasing the speed of such applications as Kanji address recognition, and it also reduces the probability of misreadings in linguistic postprocessing. Sorted sets of character candidates, ranked in increasing order of each candidate’s distance value, are used as feature vectors. A CV is defined as the a posteriori probability with respect to each rank. To obtain good quality approximations of probability density functions (PDFs), we introduce a subspace within which correct data can easily be separated from erroneous data and then estimate PDF parameters over this subspace. Next, we use an ACV as a measure for expressing a threshold for candidate acceptance in Kanji character recognition. The efficiency of the proposed method is evaluated in an experiment using IPTP CD-ROM2 Japanese address images, and a comparison with the results for a conventional method shows that a roughly 35% reduction in the number of candidates is obtained without reducing the number of correct candidates.

Optimal stroke-correspondence search method for on-line character recognition

This paper describes an optimal stroke-correspondence search method that makes possible stroke-order-free on-line character recognition. During the stroke-correspondence search process, conventional individual stroke-information regarding the shape and position of each stroke, and interstroke-information regarding the mutual relationships among strokes are both employed. The optimal path search for stroke-correspondence, being based on an optimal criterion including both intra- and interstroke-information, is systematically carried out. The reasonable level stroke-correspondence search is achieved partially by using the information regarding the actually occurring stroke-order, which does not hinder the framework of stroke-order-free recognition due to its use of statistically stable information. A large improvement in both computational time and recognition-accuracy were achieved in the current experiments.

Model-based stroke extraction and matching for handwritten Chinese character recognition

This paper proposes a model-based structural matching method for handwritten Chinese character recognition (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of each category is described in an attributed relational graph (ARG). The input character is described with feature points and line segments. The strokes and inter-stroke relations of input character are not determined until being matched with a reference character. The structural matching is accomplished in two stages: candidate stroke extraction and consistent matching. All candidate input strokes to match the reference strokes are extracted by line following and then the consistent matching is achieved by heuristic search. Some structural post-processing operations are applied to improve the stroke correspondence. Recognition experiments were implemented on an image database collected in KAIST, and promising results have been achieved.

A new linguistic decoding method for online handwritten Chinese character recognition

This paper presents a new linguistic decoding method for online handwritten Chinese character recognition. The method employs a hybrid language model which combinesN-gram and linguistic rules by rule quantification technique. The linguistic decoding algorithm consists of three stages: word lattice construction, the optimal sentence hypothesis search and self-adaptive learning mechanism. The technique has been applied to palmtop computer’s online handwritten Chinese character recognition. Samples containing millions of characters were used to test the linguistic decoder. In the open experiment, accuracy rate up to 92% is achieved, and the error rate is reduced by 68%.

Two-layer assignment method for online Chinese character recognition

A method of stroke order and number-free online recognition of Chinese characters is proposed. Both input characters and model characters are represented with complete relational graphs (CRGs). Classification of an input character can be implemented by matching its CRG against every CRG of the model base. However, efficient algorithms for graph matching are not available. Therefore, the graph-matching problem is transformed into a two-layer assignment problem and is solved with the Hungarian method. Two complexity reduction schemes are presented to save computational time. Tests demonstrate the efficiency of the proposed method.

Cursive handwritten word recognition by integrating multiple classifiers

This paper proposes a method for cursive handwritten word recognition. In the traditional research, cursive handwritten word recognition used a single method for character recognition. Our research proposes a method that integrates multiple classifiers in order to improve the word recognition rate by combining their results. Our experiment demonstrates that two classifiers outperform the word recognition rate of any single character classifier. © 2000 Scripta Technica, Syst Comp Jpn, 31(12): 23–32, 2000

A qualitative adaptation of subspace method for character recognition

A new algorithm based on the subspace method for recognizing poor-quality character images is proposed. In many cases, damage caused by photocopying or facsimile occurs accidentally. It is difficult to solve this kind of damage by statistical methods. We investigate how distribution of data changes because of blurring. Then the eigenvectors and eigenvalues that are used in the subspace method and multiple similarity method are modified according to the change. The effectiveness of the proposed algorithm is shown by the experimental results. © 2000 Scripta Technica, Syst Comp Jpn, 31(12): 1–10, 2000

On-line handwriting character recognition using direction-change features that consider imaginary strokes

We propose an on-line character recognition method that simultaneously uses both directional features, otherwise known as off-line features, and direction-change features, which we designed as on-line features. The directional features express the location and direction of each character’s coordinates. The direction-change features express where and in which direction the character’s written and unwritten imaginary stroke coordinates change, and the location of the circular parts of the character. We found suitable direction-change features with the imaginary strokes. The recognition rate was improved by our method, in comparison to the traditional method using only directional features.

Korean character recognition using a TDNN and an HMM

We present an on-line Korean character recognition method using a Time Delay Neural Network and a Hidden Markov Model. A TDNN is used to estimate a posteriori probabilities for graphemes in a character. An HMM aligns graphemes onto tentative graphemes, which are produced by heuristic segmentation points, using a modified Viterbi algorithm. The HMM contains statistic information, i.e., the frequencies of grapheme pairs and first consonants in the underlying language. We experiment on the proposed method with multi-writer cursive characters in a boxed input mode. Tests with untrained 22,500 on-line cursive characters show a recognition rate of 93.4% and speed of 1.37 s per character.

Automatic data capture for geographic information systems

We present a map interpretation system for automatic extraction of high level information from the scanned images of Chinese land register maps. Our map interpretation system consists of three main components: text/graphics separation, parcel extraction, and rotated character recognition. Our approach to text/graphics separation is based on a simple yet effective rule: the feature points of characters are more compact than those of graphics. In the parcel extraction process, the proposed algorithm traces the branches between feature points to extract polygon structure from line drawings. Our character recognition method is based on the matching of extracted strokes using a neural network. The techniques of text/graphics separation and character recognition are robust to the rotation and writing style of characters. Another advantage of our separation algorithm is that it can successfully extract a character connected to a graphical line. Experimental results have shown that the proposed system is effective for the data capture of geographic information systems.

Computer recognition of Jia Gu Wen characters

Jia Gu Wen characters, which have the features of drawings, were the most ancient graphlike Chinese characters used about 4,000 years ago. Thus, the currently available methods for Chinese character recognition are not applicable. This article proposes a two-level classification recognition method. The characters to be recognized are first abstracted to be a kind of graph, upon which the first-level classification is exercised based on their topological properties. Then the definition for generalized strokes is given. The second-level classification is exercised based on the features of these strokes. Experimental results demonstrate a recognition rate of about 94%. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 363–367, 1999

Multi-stroke relaxation matching method for handwritten chinese character recognition

Since Chinese characters can be represented by a set of basic line segments called sub-strokes, sub-strokes are often used as features to recognize handwritten Chinese Characters. However, the number of sub-strokes needed to evaluate a character are different due to handwriting variations and variations in the stroke extraction process. These differences in representation of the same character lower character recognition rate. A preliminary method to solve this problem is to merge several sub-strokes into a complete stroke. However, it is difficult to merge several sub-strokes into a correct and complete stroke because the Chinese character is not known in advance. In this paper, we propose a multi-stroke relaxation matching method to solve this problem. The proposed matching method can be divided into two parts; one is the multi-stroke relaxation process and the other is the multi-stroke select-match-pair process. The multi-stroke relaxation process will determine the optimal matching relations from the probability of each possible matching pair of sub-strokes and allow more than one of the sub-strokes to match with one or more additional sub-strokes by combining the merging steps into the relaxation process. The multi-stroke select-match-pair process is used to determine the stroke matching relation between the input and reference characters. Some experiments will be conducted to show the feasibility and correctness of the proposed algorithm. From the experimental results, we will prove that the proposed algorithm can solve the matching problem of different numbers of sub-strokes caused by handwriting variations and the stroke extraction process. For 2000 daily used Chinese characters, the actual recognition rate is 93.8 % and the cumulative recognition rate of the first five candidates is 98.9%.

A gabor filter-based method for recognizing handwritten numerals

We study a Gabor-filter-based method for handwritten numeral character recognition. The Gabor filter is based on a multi-channel filtering theory for processing visual information in the early stages of the human visual systems. The performance of the Gabor-filter-based method is demonstrated on the ETL-1 database. Experimental results show that the artificial neural-network classifier achieved the error rate of 2.34% for a test set of 7000 characters. Therefore, the Gabor-filter-based method should be considered in recognition of handwritten numeric characters.

Recognition of handwritten digits based on contour information

In this paper, a new character recognition method based on topological properties, positions of starting point, statistical analysis, and shape recognition is proposed for recognition of unconstrained handwritten digits based on various contour information. First, input digits are classified into three groups according to their topological properties. In group 1, the features of normalized length of the digit contour, the normalized area of the digit and Fourier descriptors of the outer contour of a digit are used for recognition of digits. In group 2, the relative position of outer and interior contour centroids, the position of interior contour centroid, the mean and the variance of the outer contour distance function, and Fourier descriptors of the outer contour of a digit are used for the recognition of digits. In group 3, usually there is only one digit 8, but because of the complexity of writing styles, some other digits are still classified into this group. The final recognition is based on shape comparison of the input digit with models. In the recognition process, some special models are established and used for recognition of broken digits and digits whose topological properties are destroyed. In our experiment, 1000 digits from the NIST database are used for training and 5278 unseen digits are used for testing. The recognition rate has reached 98.5% with a reliability rate of 99.09%, a substitution rate of 0.91% and a rejection rate of 0.59%.

Handwritten Chinese Character Recognition Based onPrimitive and Fuzzy Features via the SEART Neural Net Model

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.