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Relevant bibliographies by topics / On-line Recognition Algorithm

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Academic literature on the topic 'On-line Recognition Algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Journal articles on the topic "On-line Recognition Algorithm"

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Haithem Abd Al-RaheemTaha. "ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM." Diyala Journal of Engineering Sciences 5, no. 1 (2012): 79–87. http://dx.doi.org/10.24237/djes.2012.05107.

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On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly, a system based on segmentation-recognition integrated framework was developed for Arabic handwriting recognition. Secondly, the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level. The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments, and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm (Crossover, mutation, and population), the system outputs the optimal path in segmentation-recognition lattice, which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples.

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Niu, Weilong, Zan Chen, Yihui Zhu, Xiaoguang Sun, and Xuan Li. "Track Line Recognition Based on Morphological Thinning Algorithm." Applied Sciences 12, no. 22 (2022): 11320. http://dx.doi.org/10.3390/app122211320.

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In the field of intelligent driving of freight trains, determining the track line ahead of the train is an important function in the autopilot technology of such trains. Combining the characteristics of freight railway tracks, we conduct an in-depth analysis of the shortcomings of object detection technology in extracting track lines and propose an improved Zhang–Suen (ZS) thinning theory for a railway track line recognition algorithm. Through image preprocessing and single pixel thinning steps, a continuous track line is obtained and then processed by a denoising algorithm to obtain a complete track line. Experimental results show that the track extracted by our method has good continuity and less noise. It can simultaneously perform track detection on straight roads, curves and turnouts, and is suitable for changing weather conditions such as sunny daytime, mild rainy daytime, cloudy daytime, night with lamp lighting and night without lamp lighting conditions.

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Toleushova, A. T., D. M. Uypalakova, and A. B. Imansakipova. "SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM." Bulletin of Shakarim University. Technical Sciences, no. 3(7) (February 10, 2023): 47–53. http://dx.doi.org/10.53360/2788-7995-2022-1(5)-7.

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This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the recording speed. The signature has long been used to certify the authenticity of documents and verify (authenticate) an individual. In principle, the signature examination is used during the forensic examination. Signature recognition can be carried out by sequential verification of the signature to each known person. The signature recognition methodology includes a verification methodology and processing of verification results. One of the modern areas of interface improvement is the development and research of software for signature recognition and visualization. The advent of modern computer input tools has led to the emergence of a new type of online signature describing the signature creation process, not the result. Moreover, not only the coordinates of points on the line, but also a sequence of vectors of parameter values for each of the values of pressure, direction and speed of movement, the angle of adaptation of the pen and the signature time.

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Zhang, Jing Ya, Li Yang, Rong Zhao, and Long Hua Yang. "On-Line Handwriting Recognition Based on Hopfield Neural Network." Applied Mechanics and Materials 610 (August 2014): 265–69. http://dx.doi.org/10.4028/www.scientific.net/amm.610.265.

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In this paper, Discrete Hopfield Neural Network (DHNN) is adopted to realize handwritten characters recognition. First, learning samples are preprocessed including binarization, normalization and interpolation. Then pixel features are extracted and used to establish DHNN. The handwritten test samples and noise corrupted samples are finally inputted into the network to verify its recognition performance. Simulation results reveal that DHNN has good fault tolerance and disturbance rejection performance. In addition, the recognition system is realized with MATLAB neural network toolbox and GUI, which verifies the feasibility of the algorithm.

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Li, Haoyu, Stéphane Derrode, and Wojciech Pieczynski. "Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor." Sensors 19, no. 19 (2019): 4242. http://dx.doi.org/10.3390/s19194242.

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Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithms.

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Zhu, Xiaolin, and Wei Lv. "Intelligent Analysis of Core Identification Based on Intelligent Algorithm of Core Identification." Discrete Dynamics in Nature and Society 2021 (November 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5242930.

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The communication recognition of mobile phone core is a test of the development of machine vision. The size of mobile phone core is very small, so it is difficult to identify small defects. Based on the in-depth study of the algorithm, combined with the actual needs of core identification, this paper improves the algorithm and proposes an intelligent algorithm suitable for core identification. In addition, according to the actual needs of core wire recognition, this paper makes an intelligent analysis of the core wire recognition process. In addition, this paper improves the traditional communication image recognition algorithm and analyzes the data of the recognition algorithm according to the shape and image characteristics of the mobile phone core. Finally, after constructing the functional structure of the system model constructed in this paper, the system model is verified and analyzed, and on this basis, the performance of the improved core recognition algorithm proposed in this paper is verified and analyzed. From the results of online monitoring and recognition, the statistical accuracy of mobile phone core video recognition is about 90%, which has higher accuracy in mobile phone core image recognition than traditional recognition algorithms. The core line recognition algorithm based on deep learning and machine vision is effective and has a good practical effect.

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Meng, Dexiao, Wei Long, Lingxi Hu, and Linhua Jiang. "Review on line detection of wood panel images." Advances in Engineering Innovation 5, no. 1 (2024): 24–32. http://dx.doi.org/10.54254/2977-3903/5/2024061.

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In industrial, the modern intelligent degree of board counting is relatively low. Some small manufacturers still count by hand, but for large factories, manual counting needs a lot of human resources, and the accuracy of counting is low. With the rapid development of modern intelligence, image recognition is becoming more mature, some traditional algorithms keep emerging, such as Hough Transform, Fast Line Detector (FLD), Line Segment Detector (LSD) and other line detection algorithms, they have their own advantages and disadvantages, and are summarized and tested, compare which algorithm has higher accuracy and better effect in the field of board linear detection and counting. Finally, the operation mechanism, advantages and disadvantages are summarized, and the process and trend of the further optimization and development of the traditional algorithm line detection technology in the future are prospected, which provides some reference for the research in related fields.

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Wang, Dong, Tuofu Zhu, Aoyu Xie, Nian Fang, and Hongliang Liu. "Research on Image Recognition Algorithm Technology for Power Line detection." Journal of Physics: Conference Series 1732 (January 2021): 012082. http://dx.doi.org/10.1088/1742-6596/1732/1/012082.

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Bellegarda, E. J., J. R. Bellegarda, D. Nahamoo, and K. S. Nathan. "A fast statistical mixture algorithm for on-line handwriting recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 12 (1994): 1227–33. http://dx.doi.org/10.1109/34.387484.

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Miyao, Hidetoshi, Yasuaki Nakano, Atsuhiko Tani, Hirosato Tabaru, and Toshihiro Hananoi. "Printed Japanese Character Recognition Using Multiple Commercial OCRs." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 200–207. http://dx.doi.org/10.20965/jaciii.2004.p0200.

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This paper proposes two algorithms for maintaining matching between lines and characters in text documents output by multiple commercial optical character readers (OCRs). (1) a line matching algorithm using dynamic programming (DP) matching and (2) a character matching algorithm using character string division and standard character strings. The paper proposes a method that introduces majority logic and reject processing in character recognition. To demonstrate the feasibility of the method, we conducted experiments on line matching recognition for 127 document images using five commercial OCRs. Results demonstrated that the method extracted character areas with more accuracy than a single OCR along with appropriate line matching. The proposed method enhanced recognition from 97.61% provided by a single OCR to 98.83% in experiments using the character matching algorithm and character recognition. This method is expected to be highly useful in correcting locations at which unwanted lines or characters occur or required lines or characters disappear.

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Dissertations / Theses on the topic "On-line Recognition Algorithm"

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Powalka, Robert Kazimierz. "An algorithm toolbox for on-line cursive script recognition." Thesis, Nottingham Trent University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283031.

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Yu, Xun. "3D Face Recognition Based on Structural Representation." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/365949.

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3D face recognition has gained favour in the scientific community and industry due to the rapid development and decreasing cost of 3D sensors, with various novel techniques for face recognition presented in recent years. In comparison to 2D face images, 3D face images contain more explicit information, which is very useful to manage pose and illumination problems. However, the field of 3D face recognition is yet to fully mature and become widely used in industrial or commercial communities, mainly because of high error in non-cooperative and uncontrolled scenarios—particularly in challenging conditions of occlusions and partial data. Further, many existing 3D face recognition techniques require a training stage in their approach, which can suffer dramatic performance drop or even fail to work if only one training sample per person is available to the system. Thus, the one training sample issue is an important factor hindering the performance of 3D face recognition systems. In this thesis, we propose several 3D face recognition approaches to address the above issues. In the first half of this thesis, we propose two low-level structural representations 3D polygonal line chains (3DPLC) and 3D directional vertices (3D2V) to encode and match 3D faces.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>Griffith School of Engineering<br>Science, Environment, Engineering and Technology<br>Full Text

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Sun, Zhibin. "Application of artificial neural networks in early detection of Mastitis from improved data collected on-line by robotic milking stations." Lincoln University, 2008. http://hdl.handle.net/10182/665.

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Two types of artificial neural networks, Multilayer Perceptron (MLP) and Self-organizing Feature Map (SOM), were employed to detect mastitis for robotic milking stations using the preprocessed data relating to the electrical conductivity and milk yield. The SOM was developed to classify the health status into three categories: healthy, moderately ill and severely ill. The clustering results were successfully evaluated and validated by using statistical techniques such as K-means clustering, ANOVA and Least Significant Difference. The result shows that the SOM could be used in the robotic milking stations as a detection model for mastitis. For developing MLP models, a new mastitis definition based on higher EC and lower quarter yield was created and Principle Components Analysis technique was adopted for addressing the problem of multi-colinearity existed in the data. Four MLPs with four combined datasets were developed and the results manifested that the PCA-based MLP model is superior to other non-PCA-based models in many respects such as less complexity, higher predictive accuracy. The overall correct classification rate (CCR), sensitivity and specificity of the model was 90.74 %, 86.90 and 91.36, respectively. We conclude that the PCA-based model developed here can improve the accuracy of prediction of mastitis by robotic milking stations.

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Chiang, Shih-Hao, and 江世豪. "Combination of Neural Network and Genetic Algorithm for On-line Control Chart Pattern Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/d646n4.

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碩士<br>國立虎尾科技大學<br>工業工程與管理研究所<br>97<br>Statistical process control (SPC) is an important method for control process in industry. Hence, control chart is an important tool at statistical process control. The regular session presents unusual pattern in the tube charting, creates the system regulation out of control specific reason, but recognizes and the analysis control chart patterns (CCPs) has become the SPC the important topic. CCPs can be used to determine the status of system. Unnatural CCPs can be associated with a particular set of assignable causes for process variation. In recent years, artificial neural networks (ANNs) have been successfully used in the CCP recognition task. In intelligent SPC, most of researches used raw data (RB) as input vector and the other researches have used statistical feature data extracted from raw data (FB) as input vector for reducing network size. On the kind of neural network’s construction, is not easy to discover the network the best construction. Therefore this study using Genetic Algorithm (GA) the best evolved characteristic makes the union with ANNs, will enable the kind of nerve network''s identification ability to enhance again originally. We present an ANN-based approach, in which an improved hybrid training data (HB) integrates both the time series data (Raw data) and the statistical feature data (Feature data). This set of systems makes the training and the test using NeuroGenetic Optimizer(NGO), And compares whether to join the statistical characteristic value the primitive system number of better compared to not to join the statistical nature according to it identification rate of accuracy the identification rate of accuracy to come well. But the experimental result knew that the statistical characteristic value test accuracy according to us to the system number of passes according to the hypothesis disturbance rate ( 0.1-1.0 ) change situation is 98.397%, but not joins the statistical characteristic value test rate of accuracy is 98.575%. As a result of testing environment relations with the result regarding joins the statistical characteristic value not good effectiveness, instead recognizes the accuracy rate is better that the not joins the statistical characteristic value in this system.

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Shin, Young-in. "Parametric kernels for structured data analysis." Thesis, 2008. http://hdl.handle.net/2152/29669.

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Structured representation of input physical patterns as a set of local features has been useful for a veriety of robotics and human computer interaction (HCI) applications. It enables a stable understanding of the variable inputs. However, this representation does not fit the conventional machine learning algorithms and distance metrics because they assume vector inputs. To learn from input patterns with variable structure is thus challenging. To address this problem, I propose a general and systematic method to design distance metrics between structured inputs that can be used in conventional learning algorithms. Based on the observation of the stability in the geometric distributions of local features over the physical patterns across similar inputs, this is done combining the local similarities and the conformity of the geometric relationship between local features. The produced distance metrics, called “parametric kernels”, are positive semi-definite and require almost linear time to compute. To demonstrate the general applicability and the efficacy of this approach, I designed and applied parametric kernels to handwritten character recognition, on-line face recognition, and object detection from laser range finder sensor data. Parametric kernels achieve recognition rates competitive to state-of-the-art approaches in these tasks.<br>text

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Book chapters on the topic "On-line Recognition Algorithm"

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Tang, Siqi, Jin Qin, Yan Liu, Congying Han, and Tiande Guo. "An Efficient Slap Fingerprint Segmentation Algorithm Based on Convnets and Knuckle Line." In Biometric Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_25.

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Liu, Wenyin, Xiaoyu Wang, Long Tang, and Dov Dori. "Impact of Sparse Pixel Vectorization Algorithm Parameters on Line Segmentation Performance." In Graphics Recognition Recent Advances. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40953-x_31.

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Ding, Zhan, Yin Zhang, Wei Peng, Xiuzi Ye, and Huaqiang Hu. "An On-line Sketch Recognition Algorithm for Composite Shape." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_16.

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Zhang, Chengjun, Hongmei Wang, Zhiwei Wang, Faguang Wang, Minghui Min, and Shiyin Li. "Non-line-of-Sight Recognition Algorithm Based on Deep Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_66.

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Law, Tak Ming. "Automatic Detection Algorithm of Connected Segments for On-line Chinese Character Recognition." In Wavelet Analysis and Its Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45333-4_31.

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Liang, Shili, Jun Wang, Peipei Chen, Shifeng Yan, and Jipeng Huang. "Research on Image Recognition Technology of Transmission Line Icing Thickness Based on LSD Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_13.

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Thomas, Juan Carlos Rojas. "A New Clustering Algorithm Based on K-Means Using a Line Segment as Prototype." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_76.

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Lan, Guofeng, Wendeng Wei, Wen Zhao, Zhenming Zhang, Liangliang Zhao, and Wei Li. "Research on Video Real-Time Analysis and Recognition Algorithm Suitable for Transmission Line Corridor Environment." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4182-3_12.

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Sun, Xiaobo. "Design and Implementation of Classroom Teaching Line Analysis Platform Based on Pose Recognition Algorithm (MCG-CPR)." In Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2092-1_63.

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Agam, Gady, and Its'hak Dinstein. "Directional decomposition of line-drawing images based on regulated morphological operations." In Graphics Recognition Algorithms and Systems. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64381-8_36.

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Conference papers on the topic "On-line Recognition Algorithm"

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Xu, Jintao, Yu Fang, and Weiwei Gao. "Robot Visual Inertial SLAM Algorithm Based on Point-Line Features." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827291.

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Lu, Shihao, Yang Liu, Maofeng Li, et al. "The research on de-raining algorithm for degraded images of transmission line wildfire smoke based on the multistage progressive network." In International Conference on Pattern Recognition and Image Analysis, edited by Mingguang Shan and Tao Lei. SPIE, 2025. https://doi.org/10.1117/12.3056239.

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Khan, Mohammed Abdul Hafeez, Parth Ganeriwala, Siddhartha Bhattacharyya, Natasha Neogi, and Raja Muthalagu. "ALINA: Advanced Line Identification and Notation Algorithm." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00725.

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Qin, Lijuan, Shiyi Wang, and Xiong Xu. "HSV-enhanced Canny-Hough algorithm for robust lane line recognition." In International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2024), edited by Liang Hu. SPIE, 2025. https://doi.org/10.1117/12.3064448.

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Liu, Chunyao, Zhiyu Chen, Jianwei Guo, and Gang Liu. "Revolutionizing power line asset detection: a YOLOv8-driven lightweight algorithm for UAV image recognition." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055758.

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Zheng, Shunyi, Cailin Li, Guozhong Su, and Jianqing Zhang. "High-accurate line feature extraction algorithm based on line diffusion function model." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.750436.

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Zhao, Hong-dan, Guo-ying Liu, and Xu Song. "A novel line segment detection algorithm based on graph search." In Automatic Target Recognition and Navigation, edited by Jayaram K. Udupa, Hanyu Hong, and Jianguo Liu. SPIE, 2018. http://dx.doi.org/10.1117/12.2285324.

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Daifallah, Khaled, Nizar Zarka, and Hassan Jamous. "Recognition-Based Segmentation Algorithm for On-Line Arabic Handwriting." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.169.

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Yinwen Dong, Bingcheng Yuan, Hangyu Wang, and Zhaoming Shi. "A runway recognition algorithm based on heuristic line extraction." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109049.

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Liu, Yashan, Jie Zhu, Zezhou Xu, Songsi Yan, Chao Wang, and Zehui Xu. "Research on Multi-line Recognition Algorithm for Tibetan Document." In 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2022. http://dx.doi.org/10.1109/prml56267.2022.9882261.

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