Chinese Character Recognition Method Research Articles

Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition

Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition

Image iterative method for handwritten Chinese character recognition

Although accuracy and calculation time of handwritten Chinese character recognition have been greatly improved, it has not yet reached the practical application standard. Recently literatures, iterative method is used to recognize face images and good results are obtained. Therefore, image recognition is improved based on this idea. In this paper, linear combination of trigonometric functions is used as auxiliary function and image to construct discrete dynamic system. By adding bevel and shift to construct font surface and so on, the system structure is improved to solve problem of system convergence caused by large area flatness of handwritten Chinese character image, and good recognition effect is achieved. Preliminary experiments(for example, extracting 20 Chinese characters written by 9 people in the data set) are carried out, and recognition rate can reach 100% when all of them are trained, and recognition rate can reach 76.7% when each Chinese character is trained to 3 pieces; in addition, experiments were conducted on handwritten Chinese character sets changing from different angles, and the recognition rate reached 80%, which was compared with other algorithms to verify the feasibility of handwritten Chinese character recognition algorithm based on chaotic iterative trajectory characteristics of images.

Recognition of Handwritten Chinese Characters Based on Concept Learning

Many deep-learning character recognition methods have been developed over the past few years. Chinese characters are widely used in many countries; however, the deep-learning-based Chinese character recognition methods are faced with various problems, such as a large amount of data required for training, numerous parameters, and a large consumption of computing resources. Concept learning is a hominine learning approach. Unlike existing deep-learning models, conceptual model learning can be realized by using as little as one sample. This paper is the first to propose a handwritten Chinese character recognition method based on concept learning. Different from the existing image representation-based character recognition methods, the proposed method builds a meta stroke library with prior knowledge, and then, presents a Chinese character conceptual model based on stroke relationship learning using a character stroke extraction method and Bayesian program learning. During character recognition, Monte Carlo Markov chain sampling is utilized to obtain the character generation model for each character conceptual. This generation model can calculate the probability of the target and training characters being the same classification, and thereby determines the classification of the target character. The experimental results indicate that, with the proposed method, the conceptual model of each character can be built for character classification prediction using as few as one character sample. Our approach obtains better performance than the state-of-the-art methods on ICDAR-2013 competition dataset.

Generative Adversarial Networks for Open Set Historical Chinese Character Recognition

Historical Chinese character recognition has been suffering from the problem of samples labeling, not only the problem of lacking sufficient labeled training samples, but also of sample classes. So the scenario for Historical Chinese character recognition is "open set" recognition, where incomplete labeling of sample classes is present at training time, and unknown classes can be submitted to the system during testing. This paper proposes a method for open set Historical Chinese Character Recognition. For open set recognition, the features available in the training data cannot effectively characterize different kinds of unknown classes. We assume that the features which characterize unknown classes can be derived or learned from other similar data sets. We utilize an auxiliary data set combined with the open set training data set to learn good features to represent historical Chinese characters. The auxiliary data set is translated using Generative Adversarial Networks (GAN) to make sure that the translated data set is as close to the historical Chinese character dataset as possible. Then we construct a neural network for features extraction. The neural network is trained using an alternative training method with the translated auxiliary dataset and incomplete labeled historical Chinese character data set. Last, features are extracted from certain layer of the trained neural network. Unknown samples are detected using statistical modelling of the Euclidean metric between samples. Experimental results show that the proposed method is effective.

Approximative Bayes optimality linear discriminant analysis for Chinese handwriting character recognition

Discriminant subspace learning is an important branch for pattern recognition and machine learning. Among the various methods, Bayes optimality linear discriminant analysis (BLDA) has shown its superiority both in theory and application. However, due to the computational complexity, BLDA has not been applied to large category pattern tasks yet. In this paper, we propose an approximative Bayes optimality linear discriminant analysis (aBLDA) method for Chinese handwriting character recognition, which is a typical large category task. In the aBLDA, we first select a set of convex polyhedrons that are obtained by the state-of-the-art methods, then the searching zones are limited to these polyhedrons. Finally, the best of them is chosen as the final projection. In this way, the computational complexity of BLDA is reduced greatly with comparable accuracy. To find more than 1D projections, the orthogonal constraint is employed in the proposed method. The experimental results on synthetic data and CASIA-HWDB1.1 show the effectiveness of the proposed method.

Discriminative quadratic feature learning for handwritten Chinese character recognition

In this paper, we propose a feature learning method for handwritten Chinese character recognition (HCCR), called discriminative quadratic feature learning (DQFL). Based on original gradient direction feature representation, quadratic correlation between features is used to promote the feature dimensionality, then discriminative feature extraction (DFE) is used for dimensionality reduction. By combining dimensionality promotion and reduction, we can learn a much more discriminative and nonlinear feature representation, which can then boost the classification accuracy significantly. For dimensionality promotion, two types of correlation are exploited, namely, statistical correlation and spatial correlation. Statistical correlation is computed on multiple local feature vectors in different regions of the character image; while spatial correlation encodes the dependency between features of two positions. Feature correlation increases the dimensionality by over 40,000. DFE then reduces the dimensionality to less than 300 without losing discriminability. Classification is performed using nearest prototype classifier (NPC), modified quadratic discriminant function (MQDF) and discriminative learning quadratic discriminant function (DLQDF). In experiments on the CASIA-HWDB1.1 standard dataset, the proposed DQFL method improves the test accuracies of NPC, MQDF and DLQDF by 4.94%, 1.83%, and 1.82%, respectively. The test accuracy is further improved by training set expansion. On the ICDAR 2013 Chinese handwriting recognition competition dataset, the proposed DQFL+DLQDF classifier outperforms the best participating system based on deep convolutional neural network (CNN), while the test speed is much faster.

A character image restoration method for unconstrained handwritten Chinese character recognition

Despite the success of methods on constrained handwriting databases, recognition of unconstrained handwritten Chinese characters remains a big challenge. One difficulty for recognizing unconstrained handwritting is that some connected strokes are involved or some strokes are omitted. In this paper, a character image restoration method is proposed for unconstrained handwritten Chinese character recognition. In this method, the observed character image is modeled as the combination of the ideal character image with two types of noise images: the omitted stroke noise image and the added stroke noise image. To preserve the original gradient features, restoration is done on the gradient features. The estimated features are then used to discriminate similar characters. To show the effectiveness of the proposed method, we extend some state-of-the-art classifiers based on the estimated features. Experimental results show that the extended classifiers outperform the original state-of-the-art classifiers. This demonstrates that the estimated features are useful for further improving the recognition rate.

Chinese character recognition for LPR application

Letter and number recognition in license plates is widely considered a solved problem in many practical license plate recognition (LPR) systems. However, Chinese character recognition for LPR application still faces many challenges, such as more complex structure, defective character, partial occlusion, and sensitiveness to affine distortion, noise, scaling, illumination variation, contamination, blurring, and so no. In this paper, a novel method of Chinese character recognition is proposed, based on SIFT feature points clustering and matching in which a center matching strategy is designed to improve recognition efficiency. Promising experimental results demonstrate that the proposed is robust to the previous adverse factors in natural scenes and acquires higher efficiency that may meet requirements in practical application.

A Feature Extracting Method for Chinese Character Recognition of License Plate Based on Elastic Mesh and Wavelet Transform

Low-resolution Chinese character recognition of license plate is always a difficult problem. For solving it, we must think about the distinctiveness of character feature and the counting speed of method simultaneously. In this paper, we proposed a simple and effective feature extraction algorithm. First, extract the statistical feature of Chinese character based on decomposing stroke with wavelet transform. Second, apply Elastic Mesh Algorithm into extracting wavelet coefficient of decomposing stroke to get the structure information of Chinese character. The experimental results show the method is robust against low quality Chinese characters, such as skew, fuzzy, glue, distorted character, and easy to be used in actual projects with simple advantage.

An OCR Post-Processing Method Based on Dictionary Matching and Matrix Transforming

This paper describes a post-processing method for Chinese and Japanese character recognition based on dictionary. By the analysis results of recognition in the processing of OCR, we can find some segmentation and recognition errors do not conform to the rules of lexical and just recognized as the characters which its fonts approach to the scanned texts. For these errors we can deal with them by the Fix Length Segmentation Matching based on Dictionary and the Glyph Code Matrix Transforming. Through the above processing, most of the inaccurate recognitions can be corrected and by the experimental results, it can be proved that this method is an effective way to improve the recognition rate of Chinese and Japanese Character.

Research on Feature Extraction Method for Handwritten Chinese Character Recognition Based on Kernel Independent Component Analysis

Feature extraction is very difficult for handwritten Chinese character because of large Chinese characters set, complex structure and very large shape variations. The recognition rate by currently used feature extraction methods and classifiers is far from the requirements of the people. For this problem, this study proposes a new feature extraction method for handwritten Chinese character recognition based on Kernel Independent Component Analysis (KICA). Firstly, we extract independent basis images of handwritten Chinese character image and the projection vector by using KICA algorithm and then obtain the feature vector. The scheme takes full advantage of good extraction local features capability of ICA and powerful computational capability of KICA. The experiments show that the feature extraction method based on KICA is superior to that of gradient-based about the recognition rate and outperforms that of ICA about the time for feature extraction.

Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition

In handwritten Chinese character recognition, the performance of a system is largely dependent on the character normalization method. In this paper, a visual word density-based nonlinear normalization method is proposed for handwritten Chinese character recognition. The underlying rationality is that the density for each image pixel should be determined by the visual word around this pixel. Visual vocabulary is used for mapping from a visual word to a density value. The mapping vocabulary is learned to maximize the ratio of the between-class variation and the within-class variation. Feature extraction is involved in the optimization stage, hence the proposed normalization method is beneficial for the following feature extraction. Furthermore, the proposed method can be applied to some other image classification problems in which scene character recognition is tried in this paper. Experimental results on one constrained handwriting database (CASIA) and one unconstrained handwriting database (CASIA-HWDB1.1) demonstrate that the proposed method outperforms the start-of-the-art methods. Experiments on scene character databases chars74k and ICDAR03-CH show that the proposed method is promising for some image classification problems.

A Method of License Plate Chinese Character Recognition Based on Image Quality

This paper presents a method of license plate Chinese character recognition based on image quality for improving the recognition rate of low-resolution characters in license plates. Each subset of license plate Chinese character images with the same fuzzy degree are used to construct a corresponding PCA subspace, then the projection residuals distance between the Chinese character to be recognized and each subspace in the specific subspaces which are selected by character’s fuzzy degree is calculated. The class which corresponds to the subspace with the minimum distance is the recognition result. The experimental results show that the proposed method has higher recognition rate and faster recognition speed comparing to other ones.

New HHT-based handwritten Chinese character recognition method

Concerning the non-stationary characteristic of off-line handwritten Chinese characters,a new recognition method by Hilbert-Huang Transform (HHT) was presented. According to the characteristic of images of Chinese characters in this method,two-dimensional HHT for handwritten Chinese characters was developed based on the one-dimensional one to get the frequency feature of Chinese characters. Then the partial frequency feature and the global Zernike moments feature of Chinese characters were combined as a final recognition characteristic. Experimental result shows that the method is effective.

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.

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%.

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.

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.