基于数学形态学的脱机手写体汉字识别方法

笔画特征是一种有效的脱机手写体汉字的识别特征,但是笔画细化往往会造成字体的变形,提出一种无需细化预处理的笔画特征提取方法,通过数学形态学中的腐蚀、膨胀等运算,采用不同的、具有自适应性的结构元素对汉字图像进行笔画分解,并利用弹性网格提取其方向特征,最后使用广义K L变换对特征向量的维数进行压缩,去除冗余信息。实验结果验证了本方法的有效性。     

Off-line handwritten Korean character recognition based on stroke extraction and representation

In this paper, we propose an off-line recognition method for handwritten Korean characters based on stroke extraction and representation. To recognize handwritten Korean characters, it is required to extract strokes and stroke sequence to describe an input of two-dimensional character as one-dimensional representation. We define 28 primitive strokes to represent characters and introduce 300 stroke separation rules to extract proper strokes from Korean characters. To find a stroke sequence, we use stroke code and stroke relationship between consecutive strokes. The input characters are recognized by using character recognition trees. The proposed method has been tested for the most frequently used 1000 characters by 400 different writers and showed recognition rate of 94.3%.     

希尔伯特-黄变换手写体汉字识别方法

针对脱机手写体汉字的非平稳特性,提出了一种基于希尔伯特-黄变换的手写体汉字识别方法.该方法根据汉字图像的特点将一维HHT扩展为二维HHT,用以获得汉字的瞬时频率特征,并将局部的瞬时频率特征和全局的Zernike矩特征组合作为最终的识别特征.实验结果表明,该方法是有效的.     

Off-line Arabic character recognition

A personal computer-based Arabic character recognition system that performs three preprocessing stages sequentially, thinning, stroke segmentation, and sampling, is described. The eight-direction code used for stroke representation and classification, the character classification done at primary and secondary levels, and the contextual postprocessor used for error detection and correction are described. Experimental results obtained using samples of handwritten and typewritten Arabic words are presented     

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.     

Fast self-generation voting for handwritten Chinese character recognition

In this paper, a fast self-generation voting method is proposed for further improving the performance in handwritten Chinese character recognition. In this method, firstly, a set of samples are generated by the proposed fast self-generation method, and then these samples are classified by the baseline classifier, and the final recognition result is determined by voting from these classification results. Two methods that are normalization-cooperated feature extraction strategy and an approximated line density are used for speeding up the self-generation method. We evaluate the proposed method on the CASIA and CASIA-HWDB1.1 databases. High recognition rate of 98.84 % on the CASIA database and 91.17 % on the CASIA-HWDB1.1 database are obtained. These results demonstrate that the proposed method outperforms the state-of-the-art methods and is useful for practical applications.     

Parallel compact integration in handwritten Chinese character recognition

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.     

基于多分类器集成的手写体金融汉字识别

用基于遗传算法的全局优化技术动态地选择一组分类器,并根据应用的背景,采用合适的集成规则进行集成,从而综合了不同分类器的优势和互补性,提高了分类性能。实验结果表明,通过将遗传算法引入到多分类器集成系统的设计过程,其分类性能明显优于传统的单分类器的分类方法。     

Off-line Bangla handwritten word recognition: a holistic approach

Neural Computing and Applications - Due to the cursive nature, segmentation of handwritten Bangla words into characters and also recognition of the same sometimes become a very challenging problem...     

基于改进inception的脱机手写汉字识别

由于字形的复杂多变,脱机手写汉字的识别一直是模式识别的难题,深度卷积神经网络的发展为其提供了一种直接有效的解决方案。研究基于inceptions 结构神经网络的脱机手写汉字识别,提出了一种inception结构的改进方法,它具有结构更加简单、网络深度扩展更加容易、需要的训练参数量更少的优点。该方法在数据集CISIA-HWDB1.1 上进行了实验验证,采用随机梯度下降优化算法,模型达到了96.95%的平均准确率。实验结果表明,使用改进的inception结构在图像分类上具有更好的鲁棒性,更容易扩展到其他应用领域。     

Thinning and stroke segmentation for handwritten Chinese character recognition

Firstly, a thinning technique by means of stroke tracking is proposed. The method is considered to preserve the straightness of strokes and the length, which is useful for the stroke segmentation procedure on the recognition of handwritten Chinese characters.Secondly, a method for stroke segmentation, i.c. a way of breaking down a character to a set of consecutive partial strokes, is proposed, which works well owing to the favourable properties of the proposed thinning technique. The method consists of five procedures: extraction of feature pixels, calculation of stroke directions, piecewise linear representation of strokes, unification of intersections and extraction of the consecutive partial strokes.     

基于统计部首模型的联机手写汉字识别方法

利用汉字的部首层次结构有助于减小字符识别器的存储空间和提高泛化性、适应性,但部首分割一直是一个难点.提出一种新的基于部首的联机手写汉字识别方法,该方法把部首形状信息和几何信息集成到识别框架中,在组合搜索过程中利用字符-部首的层次结构字典引导部首的分割与识别,从而提高部首分割的准确率.为克服部首间的连笔,引入角点检测提取子笔划.部首识别采用统计分类器,模型参数通过自学习得到.在字符识别中,采用了2种不同的字典表示以及相应的不同搜索算法.该方法已用于左右与上下结构的字符集,实验结果表明了该方法的有效性.     

Statistical character structure modeling and its application to handwritten Chinese character recognition

This paper proposes a statistical character structure modeling method. It represents each stroke by the distribution of the feature points. The character structure is represented by the joint distribution of the component strokes. In the proposed model, the stroke relationship is effectively reflected by the statistical dependency. It can represent all kinds of stroke relationship effectively in a systematic way. Based on the character representation, a stroke neighbor selection method is also proposed. It measures the importance of a stroke relationship by the mutual information among the strokes. With such a measure, the important neighbor relationships are selected by the nth order probability approximation method. The neighbor selection algorithm reduces the complexity significantly because we can reflect only some important relationships instead of all existing relationships. The proposed character modeling method was applied to a handwritten Chinese character recognition system. Applying a model-driven stroke extraction algorithm that cooperates with a selective matching algorithm, the proposed system is better than conventional structural recognition systems in analyzing degraded images. The effectiveness of the proposed methods was visualized by the experiments. The proposed method successfully detected and reflected the stroke relationships that seemed intuitively important. The overall recognition rate was 98.45 percent, which confirms the effectiveness of the proposed methods.     

Real-time arabic handwritten character recognition

This paper considers the development of a real-time Arabic handwritten character recognition system. The shape of an Arabic character depends on its position in a given word. The system assumes that characters result from a reliable segmentation stage, thus, the position of the character is known a priori. Thus, four different sets of character shapes have been independently considered. Each set is further divided into four subsets depending on the number of strokes in the character. The system has been heavily tested and the average recognition rate has been found to be 99.6% where most of the misrecognized characters were actually written with little care. Thus, the system can be reliably used for the recognition of on-line handwritten characters entered via a graphic tablet.     

Markov random field-based statistical character structure modeling for handwritten Chinese character recognition

This paper proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures, we design the neighborhood system that accounts for the most important stroke relationships. We penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials, and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system, we use the single-site likelihood clique potentials to extract many candidate strokes from character images, and use the pairsite clique potentials to determine the best structural match between the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the KAIST character database demonstrate that MRFs can statistically model character structures, and work well in the HCCR system.     

Off-line, handwritten numeral recognition by perturbation method

This paper presents a new approach to off-line, handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. We tested our method on two worldwide standard databases of isolated numerals, namely CEDAR and NIST, and obtained 99.09 percent and 99.54 percent correct recognition rates at no-rejection level respectively. The latter result was obtained by testing on more than 170000 numerals     

基于双射变换的脱机手写数字识别方法

针对传统特征提取和分类方法速度慢、稳定性差、识别率低等特点,提出了一种基于外围结构特征提取的手写数字识别方法。该方法多次少量地提取经过双射变换后的图像外围结构特征,对每一次提取的特征结合BP神经网络生成相应的分类器,对不同特征的分类结果进行融合得出手写数字的识别结果。实验结果表明,该特征提取方法实现简单,运算量小,大大提高了脱机手写数字的识别率和效率。     

Building efficient CNN architecture for offline handwritten Chinese character recognition

Deep convolutional neural networks-based methods have brought great breakthrough in image classification, which provides an end-to-end solution for handwritten Chinese character recognition (HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computational cost and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware devices with limited computation capacity. To solve the storage problem, we propose a novel technique called weighted average pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output to complete recognition as early as possible, which reduces average inference time significantly. Experiments are performed on the ICDAR-2013 offline HCCR dataset. It is found that our proposed approach only needs 6.9 ms for classifying a character image on average and achieves the state-of-the-art accuracy of 97.1% while requires only 3.3 MB for storage.     

基于贝叶斯网络的脱机手写体汉字智能识别

针对汉字识别的超多类问题,将贝叶斯网络分类器引入小样本字符集脱机手写体汉字识别中．对手写大写数字汉字的小样本字符集构造识别系统,同时与传统的欧氏距离方法进行比较,实验表明该算法将识别率提高到92．4％,在小样本字符集脱机手写体识别中具有较强的实用性和良好的扩展性．