基于两方向动态时间规整的无分割手写汉字检测

中文文本布局复杂,汉字种类多,书写随意性大,因而手写汉字检测是一个很有挑战的问题。本文提出了一种无分割的手写中文文档字符检测的方法。该方法用SIFT定位文本中候选关键点,然后基于关键点位置和待查询汉字大小来确定候选字符的位置,最后用两个方向动态时间规整(Dynamic Time Warping, DTW)算法来筛选候选字符。实验结果表明,该方法能够在无需将文本分割为字符的情况下准确找到待查询的汉字,并且优于传统的基于DTW字符检测方法。     

Handwritten word recognition with character and inter-characterneural networks

An off-line handwritten word recognition system is described. Images of handwritten words are matched to lexicons of candidate strings. A word image is segmented into primitives. The best match between sequences of unions of primitives and a lexicon string is found using dynamic programming. Neural networks assign match scores between characters and segments. Two particularly unique features are that neural networks assign confidence that pairs of segments are compatible with character confidence assignments and that this confidence is integrated into the dynamic programming. Experimental results are provided on data from the U.S. Postal Service.     

Large vocabulary word recognition using context-dependent allophonic hidden Markov models

In this paper, we report our development of context-dependent allophonic hidden Markov models (HMMs) implemented in a 75 000-word speaker-dependent Gaussian-HMM recognizer. The context explored is the immediate left and/or right adjacent phoneme. To achieve reliable estimation of the model parameters, phonemes are grouped into classes based on their expected co-articulatory effects on neighboring phonemes. Only five separate preceding and following contexts are identified explicitly for each phoneme. By grouping the contexts we ensure that they occur frequently enough in the training data to allow reliable estimation of the parameters of the HMM representing the context-dependent units. Further improvement in the estimation reliability is obtained by tying the covariance matrices in the HMM output distributions across all contexts. Speech recognition experiments show that when a large amount of data (e.g. over 2500 words) is used to train context-dependent HMMs, the word recognition error rate is reduced by 33%, compared with the context-independent HMMs. For smaller amounts of training data the error reduction becomes less significant.     

Off-line handwritten word recognition using a hidden Markov modeltype stochastic network

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported     

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwritten Arabic words is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity.     

Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.     

Handwritten word-spotting using hidden Markov models and universal vocabularies

Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.     

Handwritten digit recognition: benchmarking of state-of-the-art techniques

This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test data set of each database, 80 recognition accuracies are given by combining eight classifiers with ten feature vectors. The features include chaincode feature, gradient feature, profile structure feature, and peripheral direction contributivity. The gradient feature is extracted from either binary image or gray-scale image. The classifiers include the k-nearest neighbor classifier, three neural classifiers, a learning vector quantization classifier, a discriminative learning quadratic discriminant function (DLQDF) classifier, and two support vector classifiers (SVCs). All the classifiers and feature vectors give high recognition accuracies. Relatively, the chaincode feature and the gradient feature show advantage over other features, and the profile structure feature shows efficiency as a complementary feature. The SVC with RBF kernel (SVC-rbf) gives the highest accuracy in most cases but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier and DLQDF give the highest accuracies. The results of non-SV classifiers are competitive to the best ones previously reported on the same databases.     

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.     

基于动态权值集成的手写数字识别方法

提出了一种基于动态权值集成的多分类器手写数字识别方法。该方法采用BP神经网络的方法,对不同的特征输入向量构建不同的神经网络分类器,通过设定动态权值,进而对不同的分类器的输出向量采用多类器集成方法进行系统集成。实验结果表明该方法具有较高的识别率和识别精度。     

On-line hand-drawn electric circuit diagram recognition using 2D dynamic programming

In order to facilitate sketch recognition, most online existing works assume that people will not start to draw a new symbol before the current one has been finished. We propose in this paper a method that relaxes this constraint. The proposed methodology relies on a two-dimensional dynamic programming (2D-DP) technique allowing symbol hypothesis generation, which can correctly segment and recognize interspersed symbols. In addition, as discriminative classifiers usually have limited capability to reject outliers, some domain specific knowledge is included to circumvent those errors due to untrained patterns corresponding to erroneous segmentation hypotheses. With a point-level measurement, the experiment shows that the proposed novel approach is able to achieve an accuracy of more than 90 percent.     

Handwritten digit recognition: investigation of normalization and feature extraction techniques

The performance evaluation of various techniques is important to select the correct options in developing character recognition systems. In our previous works, we have proposed aspect ratio adaptive normalization (ARAN) and have evaluated the performance of state-of-the-art feature extraction and classification techniques. For this time, we will propose some improved normalization functions and direction feature extraction strategies and will compare their performance with existing techniques. We compare ten normalization functions (seven based on dimensions and three based on moments) and eight feature vectors on three distinct data sources. The normalization functions and feature vectors are combined to produce eighty classification accuracies to each dataset. The comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential. The comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts. The gradient feature from gray-scale image mostly yields the best performance and the improved NCFE (normalization-cooperated feature extraction) features also perform well. The combined effects of normalization, feature extraction, and classification have yielded very high accuracies on well-known datasets.     

Handwritten Chinese character recognition using fuzzy image alignment

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.