Evaluation of soft segment modeling on a phoneme recognition system

In this research paper, an effort has been placed to convert 24 American Sign Language (ASL) signer independent, real time hand gesture alphabets into human or machine recognizable English text. In the proposed work, the ASL hand gestures used for cognition and recognition process is completely invariant to scale, luminance, gender, and distance in the complex background of indoor location. The Viola-Jones algorithm, CIE Lab color model and canny approximation to the derivative are used for proper hand segmentation. In both the cognition and recognition process, the various features such as boundary, centroid, entropy, Hu moments, Zernike moments, Gabor filters, Histogram of Oriented Gradients (HOG) and Local Phase Quantization (LPQ) are extracted from the hand gestures. The K-Nearest Neighbor (KNN), Multiclass-Support Vector Machines (M-SVM) and Decision Tree (DT) classifiers are used for classifying the hand gestures. In recognition task, these classifiers are applied independently on the same set of hand gestures to check the optimality of recognition rate and recognition time. With the detailed experimentation, it is found that, the KNN classifier achieved an average recognition rate and average recognition time of 92.71% and 0.48 s per gestures. This recognition rate and time is better and optimal compared to M-SVM and DT classifiers. Also it is an inspiring result compared to state of art techniques in real time environment by considering various invariants.

Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

The recognition of handwritten similar shape characters are still a challenging problem in HOCR system. Almost entire scripts are suffering with this kind of problem. This manuscript is considered the similar shape character problem in Devanagari script. An algorithm is proposed first to estimate the similar character pairs in Devanagari Script and 7 pairs are identified by investigating the confusion matrix. Similar shape characters have very minor difference in shape that's why at the time of recognition (classification) phase, classifier is being confused with another similar shape characters. This problem can be solved by estimate that minor difference called critical region in the similar shape characters and used critical region to extract the more features before classification phase. The critical region is estimated by Fisher discrimination function. A new kind of masking techniques used to extract the features from ISIDCHAR (standard Devanagari database) and 96.58 % recognition rate is obtained finally after getting the 81.94% improvement in similar character recognition.

: Problem Statement: Pecan weevil is one of the most destructive pests of Oklahoma. The scope of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Approach: The recognition methods used in this study are based on template matching. Five recognition methods were implemented: Normalized cross-correlation, Fourier descriptors, Zernike moments, String matching and Regional properties. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 74 other insects which typically exist in pecan habitat. Results: It was found that Region-based methods were better in representing and recognizing biological objects such as insects. Different recognition rates were obtained at different order of Zernike moments. The optimum result among the tested orders of Zernike moments was found to be at the order 3. The results also showed that using different number of Fourier descriptors may not significantly increase the recognition rate of this method. Conclusion: The most robust and reliable recognition rate was achieved when the Zernike moments and Region properties recognition methods were used in a combination. A positive match from either of these two independent tests would yield reliable results. Therefore, 100% recognition could be achieved by adopting the proposed algorithm. The processing time for such recognition is 0.44 sec.

In order to apply deep learning to the field of image recognition, the basic principle, training process and model structure of deep belief networks (DBNs) in deep learning are analysed. For small samples, samples are down-sampled at the pretraining stage. In the parameter fine-tuning stage, random dropout is introduced, and the hidden layer nodes are cleared out randomly to keep the weights unchanged. The results show that the layered training mechanism of DBNs greatly reduces the difficulty of training and the training time. In the small sample, after introducing the down-sampling and random dropout, the deep belief network has a good improvement in recognition rate and time consumption, and the over fitting phenomenon is effectively alleviated.

This paper presents a new method for facial expression recognition using divided local binary pattern features. Motivated by Fourier’s parity decomposition, we decompose a LBP descriptor into two subdescriptors. This method reduces the amount of data of the feature extraction, reducing the data dimension effectively. Experimental results on JAFFE facial expression database show that the proposed method can get significant performance improvement in recognition rate, reduce the recognition time greatly.

The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

Remote controls are commonly used today as an interface for almost all consumer electronic equipment. Different home appliances use a multitude of remote controllers, offering a variety of problems: confusion in the different button layout, spending time in looking for the right remote, problems in replacement when destroyed and so on. Hand gestures have been one of the oldest and natural methods of human interaction. It is considered as an interaction medium that can possibly deliver more intuitive, creative and natural method for communication between human and a machine. The researchers choose to create an android based hand gesture controlled interface system for home appliances that serves as an alternative to existing remote controllers. The camera of the android device will be used to detect static hand gestures for processing. The gathered data will be compared to a database of recognizable static hand gestures to execute commands on certain home appliances using infrared light. After series of testing and evaluation, the system is able to execute control functions to the home appliances indicated by specific hand gestures. The system required correct execution of the corresponding static gesture to properly execute the command functions of the appliances with proper lighting and illuminance for proper recognition else it would result into loss of accuracy and false detection. For the system to be able to communicate or receive information the transmission of the sender (IR) should be in line of sight to the receivers (IR) with six (6) meters range between the appliances and the mobile phone. A 100% success rate of gesture recognition was observed in Samsung Galaxy S4 with 2.26451 seconds recognition time while there were only three failures of hand gesture detection using Samsung Galaxy Note 3 with 2.6991 seconds recognition time.

This paper focuses on the problem of environmental noises in human-human communication and in automatic speech recognition. To deal with this problem, the use of alternative acoustic sensors -which are attached to the talker and receive the uttered speech through skin or bones- is investigated. In the current study, throat microphones and ear bone microphones are integrated with standard microphones using several fusion methods. The results obtained show that the recognition rates in noisy environments are drastically increased when these sensors are integrated with standard microphones. Moreover, the system does not show any recognition degradations in clean environments. In fact, recognition rates also increase slightly in clean environments. Using late fusion to integrate a throat microphone, an ear bone microphone, and a standard microphone, we achieved a 44% relative improvement in recognition rate in a noisy environment and a 24% relative improvement in recognition rate in a clean environment.

On-line Chinese character recognition using ART-based stroke classification

A pattern recognition method based on modified perception algorithm was proposed in this paper. The principle of this method is minimizing the sum of distance between the wrong classified samples and the interface of different categories, and then the iteration was done with information contained in the minimized errors. The experiment shows that the proposed method can classify the images from Yale, ORL and actual face databases accurately and effectively. The main advantage of the method is that the algorithm is convenient for computer programming, and the recognition rate and recognition time have been distinctly developed.

In this work, the performance of Tamil phoneme recognition system was improved by using language models at the recognition phase. The speech signals were segmented at phonetic level using the language models to identify segmentation points. The phoneme level recognition was done using a similarity measure, based on the acoustic characteristics of the phoneme signal. The errors in the recognized phoneme sequences were then detected and corrected using a language model designed based on the integration of a variable length phoneme language model and inter-word hybrid language model. The performance of phoneme recognition system was improved by using the language models

With the rapid development of the Internet short-video platform, microvideo is increasingly attracting users because of its small traffic and time period. However, the content of microvideo on the Internet is complex, with illegal information. This research is an improved algorithm based on target detection technology, aiming at the characteristics of difficult character recognition in microvideo. The algorithm combines Gabor wavelet transform algorithm and 2D principal component analysis (PCA) algorithm and makes full use of video perception technology to realize character recognition in microvideo. Through MATLAB simulation analysis, it can be seen that the accuracy of the optimization algorithm proposed in this paper is 86.34% and the recognition time is 34.28s. Compared with the traditional PCA algorithm and artificial neural network (ANN) algorithm, the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency. Received: 28 October 2021 | Revised: 15 December 2021 | Accepted: 21 December 2021 Conflicts of Interest The author declares that he has no conflicts of interest to this work.

Gait recognition stands as a pivotal biometric technology in individual identification, yet its real-world implementation faces challenges stemming from intra-subject disparities. The task of extracting consistent features to distinguish among various subjects becomes onerous due to factors such as image noise and magnitude divergence, significantly impacting recognition accuracy. In addressing this hurdle, we introduce a groundbreaking approach known as the Spline Magnitude Disparity Cross-Correlated Deep Network, designed to optimize gait recognition efficiency. Our method, the Spline Magnitude Disparity Cross-Correlated Deep Network, operates through two key steps: B-Spline magnitude disparity deformation (BS-MDD) registration and cross-correlated long-short gait recognition modeling. The BS-MDD algorithm employs free-form deformation to approximate the magnitude divergence in gait input, enhancing viewpoint optimization and contributing to the development of the cross-correlated model. By focusing on preserving high-output recognition gates while eliminating forget gates, our approach achieves a heightened recognition rate. Evaluation on the widely utilized CASIA B dataset showcases the superiority of our proposed method over state-of-the-art alternatives in terms of the true positive rate, false-positive rate, recognition time, and overall recognition rate. Notably, our approach elevates the true positive rate by 5% and reduces the false-positive rate by 4%. These results underscore the high effectiveness of our method, demonstrating its capacity to substantially improve the accuracy of gait recognition in practical applications.”

A robust method for coarse classifier construction from a large number of basic recognizers for on-line handwritten Chinese/Japanese character recognition