Recognition of hand-printed characters based on structural description and inductive logic programming

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example.

The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the average correct recognitions rate obtained using cross-validation was 86.65%.     

Recognition of handprinted Hebrew characters using features selected in the Hough transform space

Automatic identification of handprinted Hebrew characters is described in this paper. The recognition model devised constitutes a multi-stage system. In the first stage a coarse classifier allocates the input patterns into one of 17 categories, based on the number and the location of end points within predetermined regions in the characters matrix. The second stage uses features extracted in the Hough transform space to classify characters assigned to each of 16 categories. The remaining one category, composed of similar, square-like (rotated L shape) classes, is recognized by structural analysis and a statistical classifier. An additional step of postprocessing is added to compensate for the sensitivity of the Hough transform to the existence of similar classes within some of the categories. Experiments were conducted with a multi-author (40 writers) data base. An average recognition rate of 86.9% was observed for the system. This compared favorably with the results of two other recognition methods.     

卷积神经网络的多字体汉字识别

目的 多字体的汉字识别在中文自动处理及智能输入等方面具有广阔的应用前景,是模式识别领域的一个重要课题。近年来,随着深度学习新技术的出现,基于深度卷积神经网络的汉字识别在方法和性能上得到了突破性的进展。然而现有方法存在样本需求量大、训练时间长、调参难度大等问题,针对大类别的汉字识别很难达到最佳效果。方法 针对无遮挡的印刷及手写体汉字图像,提出了一种端对端的深度卷积神经网络模型。不考虑附加层,该网络主要由3个卷积层、2个池化层、1个全连接层和一个Softmax回归层组成。为解决样本量不足的问题,提出了综合运用波纹扭曲、平移、旋转、缩放的数据扩增方法。为了解决深度神经网络参数调整难度大、训练时间长的问题,提出了对样本进行批标准化以及采用多种优化方法相结合精调网络等策略。结果 实验采用该深度模型对国标一级3 755类汉字进行识别,最终识别准确率达到98.336%。同时通过多组对比实验,验证了所提出的各种方法对改善模型最终效果的贡献。其中使用数据扩增、使用混合优化方法和使用批标准化后模型对测试样本的识别率分别提高了8.0%、0.3%和1.4%。结论 与其他文献中利用手工提取特征结合卷积神经网络的方法相比,减少了人工提取特征的工作量;与经典卷积神经网络相比,该网络特征提取能力更强,识别率更高,训练时间更短。     

Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms

Having a reliable approximation of heating load (HL) and cooling load (CL) is a substantial task for evaluating the energy performance of buildings (EPB). Also, the appearance of soft computing techniques has made many traditional methods antiquated. Thus, the main effort of this study was to evaluate the capability of several learning methods for appraising the HL and CL of a residential building. To this end, a proper dataset consisting of eight influential factors was provided. To simplify the problem, we executed feature validity by using a correlation-based feature subset selection (CfsSubsetEval) technique. The results of this process showed that wall area, overall height, orientation and glazing area have the most significant impact on the HL and CL simulation. After preparing the suitable dataset, sixteen learning methods namely, elastic net (EN), Gaussian process regression (GPR), least median of squares regression (LMSR), multiple linear regression (MLR), multi-layer perceptron regression (MPR), multi-layer perceptron (MLP), radial basis function regression (RBFR), sequential minimal optimization regression (SMOR), functions XNV, lazy K-star, lazy LWL, rules decision table (RDT), M5Rules, alternating model tree (AMT), directional path consistency (DPC), and Random Forest (RF) were developed in Weka environment to forecast the HL and CL variables. Referring to the results, it was concluded that RF, lazy K-star, RDT and AMT outperform other predictive models. Also, comparing the results with the results of the previous studies showed that the applied feature reduction not only did not disturb the learning process but also has enhanced the performance of models. Also, due to the excellent accuracy of the MLP, a formula was derived from the optimized structure of it to predict the HL and CL variables.     

Neural Network Recognition of Hand-printed Characters

Character recognition systems can contribute tremendously to the advancement of the automation process, and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications.The main theme of this paper is the automatic recognition of hand-printed Latin characters using artificial neural networks in combination with conventional techniques. This approach has a number of advantages: it combines rule-based (structural) approach for feature extraction and non-linea classification tests for recognition; it is more efficient for large and complex data sets; feature extraction is inexpensive and execution time is independent of handwriting style and size. The technique can be divided into three major steps: The first step is pre-processing in which the original image is transformed into a binary image utilising a 300 dpi scanner and then thinned using a parallel thinning algorithm. Second, the image-skeleton is traced from left to right in order to build a binary tree. Some primitives, such as Straight lines, Curves and Loops, are extracted from the binary tree. Finally, a three layer artificial neural network is used for character classification. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct average recognition rate obtained using cross-validation was 86%.     

一种新的基于细化的汉字笔画抽取方法及其在汉字识别中的应用

文章提出了一种新的基于细化的汉字笔画抽取方法,并把笔画统计特征用于汉字的识别。实验结果表明,该方法可有效地抽取出汉字的笔画并可成功地用于汉字的识别。     

Sleep posture recognition based on machine learning: A systematic review

Background:In recent years, the application of artificial intelligence in the field of sleep medicine has rapidly emerged. One of the main concerns of many researchers is the recognition of sleep positions, which enables efficient monitoring of changes in sleeping posture for precise and intelligent adjustment. In sleep monitoring, machine learning is able to analyze the raw data collected and optimizes the algorithm in real-time to recognize the sleeping position of the human body during sleep.Methodology:A detailed search of relevant databases was conducted through a systematic search process, and we reviewed research published since 2017, focusing on 27 articles on sleep recognition.Results:Through the analysis and study of these articles, we propose several determinants that objectively affect sleeping posture recognition, including the acquisition of sleep posture data, data pre-processing, recognition algorithms, and validation analysis. Moreover, we analyze the categories of sleeping postures adapted to different body types.Conclusion:A systematic evaluation combining the above determinants provides solutions for system design and rational selection of recognition algorithms for sleep posture recognition, and it is necessary to regularize and standardize existing machine learning algorithms before they can be incorporated into clinical monitoring of sleep.     

正规化模糊神经网络及在手写体汉字识别中的应用

为改善手写体汉字识别的性能,提出了一种基于正规化模糊神经网络的识别方法。针对网络结构的优化问题给出了网络模型的规则层节点的选取方法和相应的反传播学习规则。该算法能够充分利用专家制订的“if-then”规则,完善网络的推理结构,提高网络的识别能力,减少噪声因素的影响。实验表明此方法对手写体汉字识别问题具有良好的适应性和实用性。该方法指出了一条进一步提高手写体汉字系统性能的新途径。     

并行特征融合在金融手写汉字识别中的应用

针对金融票据自动识别应用中的脱机手写体汉字识别进行特征提取的研究,首先提出了用Gabor特征和Zemike矩特征来分别表征汉字的局部特征和全局特征。针对传统的串行特征融合方法的缺陷,提出了一种并行特征融合方法,将Gabor特征和Zemike矩特征组合成新的特征向量,然后使用广义K-L变换对新特征向量的维数进行压缩,去除冗余信息。实验结果验证了该方法的有效性。     

Designing learning objects that afford learners the experience of important variations in Chinese characters

Effective teaching should focus the attention of learners to its essential aspects. It follows that instructional software can be designed in such a way that allows learners to experience the important variations in the critical aspects of the content to be learned. This paper reports on the experience of designing such special kinds of instructional learning objects for the learning of Chinese characters. The design of these learning objects takes into consideration not only what Chinese characters are all about but also how learners commonly make errors while they learn to write the characters. Out of the analysis of these learners' errors, variations in the structural features of Chinese characters were pulled out and embodied in the design of the learning objects. Learners tinkering with the learning objects can thus implicitly develop a sense of the structural features or regularity of Chinese characters, which most importantly should prepare the learners to learn more new characters in the future. The main proposal of this paper is the notion of this variation‐affording instructional software that allows learners to attend to the essential aspects of what is to be learned. Furthermore, the idea of the learning object also differs from other instructional software in its small, self‐contained and reusable nature, such that teachers can flexibly embed the learning objects into their own teaching materials.     

一种基于汉字笔画的文本零水印算法

针对基于汉字的文本嵌入比较困难,水印不可见性不高,鲁棒性不高等特点,提出一种基于汉字笔画数的文本零水印新算法,算法通过统计各汉字在文本中的使用频率,即可得到使用频率最高的汉字,使用频率最高汉字的笔画数作为文本特征序列,用此序列与水印信息运算生成一组注册码。实验结果表明,该算法实现简单,具有很好的不可见性和鲁棒性。     

A comparison of machine learning techniques for customer churn prediction

We present a comparative study on the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications industry. In the first phase of our experiments, all models were applied and evaluated using cross-validation on a popular, public domain dataset. In the second phase, the performance improvement offered by boosting was studied. In order to determine the most efficient parameter combinations we performed a series of Monte Carlo simulations for each method and for a wide range of parameters. Our results demonstrate clear superiority of the boosted versions of the models against the plain (non-boosted) versions. The best overall classifier was the SVM-POLY using AdaBoost with accuracy of almost 97% and F-measure over 84%.     

Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection

Prostate cancer is a highly incident malignant cancer among men. Early detection of prostate cancer is necessary for deciding whether a patient should receive costly and invasive biopsy with possible serious complications. However, existing cancer diagnosis methods based on data mining only focus on diagnostic accuracy, while neglecting the interpretability of the diagnosis model that is necessary for helping doctors make clinical decisions. To take both accuracy and interpretability into consideration, we propose a stacking-based ensemble learning method that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules. For this purpose, a multi-objective optimization algorithm is devised to maximize the classification accuracy and minimize the ensemble complexity for model selection. As for model combination, a random forest classifier-based stacking technique is explored for the integration of base learners, i.e., decision trees. Empirical results on real-world data from the General Hospital of PLA demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods in terms of the classification accuracy, sensitivity and specificity. Moreover, the results reveal that several diagnostic rules extracted from the constructed ensemble learning model are accurate and interpretable.     

Recognizing yield patterns through hybrid applications of machine learning techniques

Yield management in semiconductor manufacturing companies requires accurate yield prediction and continual control. However, because many factors are complexly involved in the production of semiconductors, manufacturers or engineers have a hard time managing the yield precisely. Intelligent tools need to analyze the multiple process variables concerned and to predict the production yield effectively. This paper devises a hybrid method of incorporating machine learning techniques together to detect high and low yields in semiconductor manufacturing. The hybrid method has strong applicative advantages in manufacturing situations, where the control of a variety of process variables is interrelated. In real applications, the hybrid method provides a more accurate yield prediction than other methods that have been used. With this method, the company can achieve a higher yield rate by preventing low-yield lots in advance.     

A Form-Correcting System of Chinese Characters Using a Model of Correcting Procedures of Calligraphists

A support system for form-correction of Chinese Characters is developed based upon a generation model SAM,and its feasibility is evaluated.SAM is excellent as a model for generating Chinese characters,but it is difficult to determine appropriate parameters because the use of calligraphic knowledge is needed.by noticing that calligraphic knowledge of calligraphists is included in their corrective actions, we adopt a strategy to acquire calligraphic knowledge by monitoring,recording and analyzing corrective actions of calligraphists,and try to realize an environment under which calligraphists can easily make corrections to character forms and which can record corrective actions of calligraphists without interfering with them.In this paper,we first construct a model of correcting procedures of calligraphists,which is composed of typical correcting procedures that are acquired by extensively observing their corrective actions and interviewing them,and develop a form-correcting system for brush-written Chinese characters by using the model.Secondly,through actual correcting experiments,we demonstrate that parameters within SAM can be easily corrected at the level of character patterns by our system,and show that it is effective and easy for calligraphists to be used by evaluating effectiveness of the correcting model,sufficiency of its functions and execution speed.     

基于特征反馈的手写体汉字识别系统研究

针对传统基于开环的汉字识别系统不完全符合人类识字过程的问题,构建了一种具有反馈结构的仿人智能识别系统。该系统根据待识别汉字的多模态定性识别结果来选择最佳的首轮识别方案,在完成识别之后,提取广义识字误差对候选字进行可信度判断和反馈校正。设计了3种广义识字误差,通过对这3种广义识字误差的类型和数值进行定性与定量相结合的分析,建立了识别结果的可信度评价指标体系和反馈校正决策机制。仿真实验结果验证了方法的可行性。     

Prediction of TBM operation parameters using machine learning models based on BPSO

Rational parameters of TBM (Tunnel Boring Machine) are the key to ensuring efficient and safe tunnel construction. Machine learning (ML) has become the main method for predicting operating parameters. Grid Search and optimization algorithms, such as Particle Swarm Optimization (PSO), are often used to find the hyper parameters of ML models but suffer from excessive time and low accuracy. In order to efficiently construct ML models and enhance the accuracy of predicting models, a BPSO (Beetle antennae search Particle Swarm Optimization) algorithm is proposed. Based on the PSO algorithm, the concept of BAS (Beetle Antennae Search) is integrated into the updating process of an individual particle, which improves the random search capability. The convergence of the BPSO algorithm is discussed in terms of inhomogeneous recursive equations and characteristic roots. Then, based on the proposed BPSO prototype, a hybrid ML model BPSO-XGBoost (eXtreme Gradient Boosting) is proposed. We applied the model to the Hangzhou Central Park tunnel project for the prediction of screw conveyer rotational speed. Finally, our model is compared with existing methods. The experimental results show that the BPSO-based model outperforms other traditional ML methods. The BPSO-XGBoost is more accurate than PSO-XGBoost and BPSO-RandomForest for predicting the speed. Also, it is verified that the hyper parameters optimized by the BPSO are better than those optimized by the original PSO. The comprehensive prediction performance ranking of models is as follows: BPSO-XGBoost > PSO-XGBoost > BPSO-RF > PSO-RF. Our models have preferable engineering application value.     

基于小波变换和分形维数的车牌汉字识别

提出一种基于小波变换与分形维数的车牌汉字识别方法.对字符图像进行预处理和小波变换,应用改进的微分盒维法计算图像分形盒维值,并构造特征向量,利用支持向量机分类器对字符进行分类与识别.实验结果表明,该方法对模糊字符的识别具有鲁棒性,可提高汉字识别率.     

Intelligent compilation of patent summaries using machine learning and natural language processing techniques

Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.