Classification of characters by man and by machine

In this paper the performance of a nearly optimal system for character recognition is compared to human performance on the same data set. The recognition system uses a linear feature extraction method which is superior to discrete Karhunen-Loeve expansion. The experiments consider binary and multiple classification of handprinted characters, binary classification of similar characters of one font corrupted by additive white normal noise, and multiple classification of truncated handprinted characters. It turns out that the human visual system is superior in recognizing handprinted characters and inferior in the case of single font characters with additive noise.     

Research on machine recognition of handprinted characters

Machine recognition of handprinted Chinese characters has recently become very active in Japan. Both from the practical and the academic point of view, very encouraging results are reported. The work is described systematically and analyzed in terms of so-called feature matching, which is likely to be the mainstream of the research and development of machine recognition of handprinted Chinese characters. A database, ETL8 (881 Kanji, 71 hirakana, and 160 variations for each category), is explained, on which many experiments were performed. Recognition rates reported using this database can be compared, and so somewhat qualitative evaluation of these methods is described. Based on the comparative study, the merits and demerits of both feature and structural matching are discussed and some future directions are mentioned.     

Recognizing machines with parametric and nonparametric learning methods using contextual information

Pattern recognition systems using information from the pattern which follows the present pattern are discussed. Parametric learning methods in the supervised and unsupervised machines are proposed and compare favorably with conventional methods. Furthermore, a semilinear machine with a nonparametric learning method is considered. The results of computer experiments with artificially generated data and with handprinted alphanumeric characters are given to show that the approach we adopt is quite useful for recognition of Markovian patterns.     

基于跨模态深度度量学习的甲骨文字识别

甲骨文字图像可以分为拓片甲骨文字与临摹甲骨文字两类.拓片甲骨文字图像是从龟甲、兽骨等载体上获取的原始拓片图像,临摹甲骨文字图像是经过专家手工书写得到的高清图像.拓片甲骨文字样本难以获得,而临摹文字样本相对容易获得.为了提高拓片甲骨文字识别的性能,本文提出一种基于跨模态深度度量学习的甲骨文字识别方法,通过对临摹甲骨文字和...     

Adaptive recognition of Chinese characters: imitation ofpsychological process in machine recognition

This paper is focused on imitation of human psychological process in machine recognition of Chinese characters. Some results of research on human Chinese character recognition have been discussed and unified into a compound mechanism with an adaptive and self-developing nature. A machine imitation model has been proposed for Chinese character recognition with different routines. By some simplification but with the crucial feature of the model being retained, an experimental system for handprinted Chinese character recognition based on the novel concept has been built. Experimental results have shown that the associated routines continuously improve their performance during their work even after supervised training is halted. The routine of the global pattern approach eventually learns most of the classes and the recognition process gradually shifts from the subpattern approach to the global pattern approach     

手写体汉字识别问题综论

本文在讨论了人对汉字的认知心理试验研究结果之后,认为人类的汉字识别是经由整字属性及分层结构的多种途径复合而成的。这些途径中的大多数都可以在手写体汉字的机器识别中模仿运用。由此可把汉字的机器识别方法归纳为整模式法和子模式法两个大类,对它们的有利方面和困难方面进行了讨论;对影响其性能的因素进行了分析;对识别率以及它与识别字集大小和后处理等的关系提出了评估的问题。     

The effect of inflation‐induced demand and trade credit on ordering policy of exponentially deteriorating and imperfect quality items

This paper develops an inventory model for exponentially deteriorating and imperfect quality items when a trade credit is offered by the supplier. As it has been seen that the changing rates of inflation do not just have an effect on the ordered quantity but they also stimulate the demand for products, so this paper considers the combined effect of inflation, deterioration, imperfect quality and ‘’ trade credit policy on the economic ordering policy using a discounted cash flow (DCF) approach. In the study, mathematical models are derived under two different circumstances, i.e., Case I: When the discount offered by the supplier with a condition of making the payment by a specified period, M₁ is accepted; and Case II: Discount is not taken and instead the payment is made by the end of the full credit period M (M > M₁). Finally, a numerical example demonstrating the applicability of the proposed model has been included.     

Recognition of handprinted Kanji characters by the stroke matching method

This paper describes an improved stroke matching method for the recognition of handprinted Kanji characters. Using the stroke feature and two additional global features, a recognition rate of over 90% has been obtained for about 1000 Kanji characters.     

Representation and recognition of handwritten digits usingdeformable templates

We investigate the application of deformable templates to recognition of handprinted digits. Two characters are matched by deforming the contour of one to fit the edge strengths of the other, and a dissimilarity measure is derived from the amount of deformation needed, the goodness of fit of the edges, and the interior overlap between the deformed shapes. Classification using the minimum dissimilarity results in recognition rates up to 99.25 percent on a 2,000 character subset of NIST Special Database 1. Additional experiments on an independent test data were done to demonstrate the robustness of this method. Multidimensional scaling is also applied to the 2,000×2,000 proximity matrix, using the dissimilarity measure as a distance, to embed the patterns as points in low-dimensional spaces. A nearest neighbor classifier is applied to the resulting pattern matrices. The classification accuracies obtained in the derived feature space demonstrate that there does exist a good low-dimensional representation space. Methods to reduce the computational requirements, the primary limiting factor of this method, are discussed     

Improved Scale-Invariant Moments for Deformation Digits

This paper presents improved scaled-invariant moments for digits with deformations. We claim that deformation digits would be digits with improper shapes, unconstrained styles of writing and different orientations. An experimental evaluation of utilizing various moments order as pattern features in recognition of handprinted and handwritten digits have been carried out using improved scaled-invariant moments. We use scale-invariant moments of order 2 for the numerator and order 4 for the denominator while preserving the scale factor of the same order. These moments have been used as feature extraction for digits with various orientations. Digits are rotated clockwise and counter clockwise of 45 degree, and using unequal scaling in x and y directions. As a comparison, we generate geometric moment invariants on handwritten digits. We train improved scaled-invariant moments using a standard backpropagation model and modified backpropagation model for classifications. We found that the results are promising with improved scaled-invariant moments of higher order, and the classifications of digits are successfully recognized.     

Hierarchical classifier design using mutual information

A nonparametric algorithm is presented for the hierarchical partitioning of the feature space. The algorithm is based on the concept of average mutual information, and is suitable for multifeature multicategory pattern recognition problems. The algorithm generates an efficient partitioning tree for specified probability of error by maximizing the amount of average mutual information gain at each partitioning step. A confidence bound expression is presented for the resulting classifier. Three examples, including one of handprinted numeral recognition, are presented to demonstrate the effectiveness of the algorithm.     

APPLICATION OF SYSTEM RECONSTRUCTABILITY ANALYSIS ON RESEARCH OF RELATIONS BETWEEN PRICE AND FINANCIAL-ECONOMIC FACTORS

This paper gives a system factor reconstructability analysis model with a residential living cost index and a retail price index of consumer goods as criteria. The impact and cumulative dynamical effects are considered using the following factors: total investment of capital construction, quantity of money M ₀ in circulation, money supply M ₁ and M ₂, total cash payment of the bank, payment of the bank for wages and other personal cash payments, enterprise deposit, total amount of export and import, and gross domestic products. It also constructs a model to study the impacts of some ratios and differences between the increased rates of these factors on the increased rate of the price index, and constructs similar models to study the impacts of the studied year factor increase rates on the price index rates. A model for relations of monthly fluctuations of these factors and the price index is given. Lastly, by studying the results of these models, this paper proposes comprehensive conclusions about the relations of price and financial-economic factors.     

A comparison of connectionist models of music recognition and human performance

Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed.     

Pattern recognition by sequential sweeping along preferred directions†

This paper deals with a novel method of pattern recognition, in which recognition is effected by sequential sweeping along preferred directions. The chosen set of measurement directions (x ₁, x ₂, … x _n) are first ordered according to preference and then the axes are swept sequentially in that order. An algorithm is proposed which gives a code representation for the pattern clusters in an n-dimensional pattern space. Two models are proposed for the recognizer. It is shown that the method is capable of 100% recognition of learning patterns, under the assumption that no two patterns belonging to different categories occupy the same pattern point in the pattern space.

A further advantage of the method is that out of an initially chosen set of measurements (characteristics) the recognizer makes use only of an important sub-set. Thus a sort of ‘measurement selection ’ is inherent in this type of pattern recognizer.     

Heuristic algorithms for two machine re-entrant flow shop

This paper focuses on a two machine re-entrant flow shop scheduling problem with the objective of minimizing makespan. In the re-entrant flow shop considered here, each job has the processing route (M₁, M₂, M₁, M₂, …, M₁, M₂). We present heuristic algorithms, some are modified from existing algorithms and some are newly developed. Extensive computational experiments are performed to evaluate the performance of the heuristics. Results of the experiments show that the performance of heuristics is significantly affected by the distribution of workloads on machines and some of them are excellent.     

CHITRA: Cognitive handprinted input-trained recursively analyzing system for recognition of alphanumeric characters

A novel system for recognition of handprinted alphanumeric characters has been developed and tested. The system can be employed for recognition of either the alphabet or the numeral by contextually switching on to the corresponding branch of the recognition algorithm. The two major components of the system are the multistage feature extractor and the decision logic tree-type catagorizer. The importance of good features over sophistication in the classification procedures was recognized, and the feature extractor is designed to extract features based on a variety of topological, morphological and similar properties. An information feedback path is provided between the decision logic and the feature extractor units to facilitate an interleaved or recursive mode of operation. This ensures that only those features essential to the recognition of a particular sample are extracted each time. Test implementation has demonstrated the reliability of the system in recognizing a variety of handprinted alphanumeric characters with close to 100% accuracy.Most of this work was carried out at the School of Automation, Indian Institute of Science, Bangalore, India.     

Hierarchical visual event pattern mining and its applications

In this paper, we propose a hierarchical visual event pattern mining approach and utilize the patterns to address the key problems in video mining and understanding field. We classify events into primitive events (PEs) and compound events (CEs), where PEs are the units of CEs, and CEs serve as smooth priors and rules for PEs. We first propose a tensor-based video representation and Joint Matrix Factorization (JMF) for unsupervised primitive event categorization. Then we apply frequent pattern mining techniques to discover compound event pattern structures. After that, we utilize the two kinds of event patterns to address the applications of event recognition and anomaly detection. First we extend the Sequential Monte Carlo (SMC) method to recognition of live, sequential visual events. To accomplish this task we present a scheme that alternatively recognizes primitive and compound events in one framework. Then, we categorize the anomalies into abnormal events (never seen events) and abnormal contexts (rule breakers), and the two kinds of anomalies are detected simultaneously by embedding a deviation criterion into the SMC framework. Extensive experiments have been conducted which demonstrate that the proposed approach is effective as compared to other major approaches.