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

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Published: 3 June 2025

Last updated: 13 July 2025

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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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Jian Wang, Jian Wang, Dong-Liang Fan Jian Wang, Jin-Ping Du Dong-Liang Fan, Lei Geng Jin-Ping Du, and Ya-Jin Hou Lei Geng. "Research on Artificial Intelligence Detection Method of Lithium Battery Surface Defects for Production Line." 電腦學刊 34, no. 2 (2023): 203–14. http://dx.doi.org/10.53106/199115992023043402015.

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<p>Lithium batteries are widely used in new energy vehicles and electronic equipment. Aiming at the typical defects that are easy to occur in the production process of lithium batteries, this paper improves the performance and recognition accuracy of the algorithm by integrating void convolution and attention mechanism into the YOLOv5 basic framework. At the same time, whale algorithm is used to automatically optimize the algorithm parameters in the process of optimization. Finally, through simulation experiments. This method realizes the rapid and accurate identification of lithium battery defects in the rapid production process of automatic production line.</p> <p>&nbsp;</p>

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Zhao, Wei Dong, Chang Liu, and Tao Yan. "Incremental Tensor Principal Component Analysis for Image Recognition." Advanced Materials Research 710 (June 2013): 584–88. http://dx.doi.org/10.4028/www.scientific.net/amr.710.584.

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Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms.

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Zhu, XiaoLei. "Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm." Wireless Communications and Mobile Computing 2022 (May 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/1576382.

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Aiming at the problems of low recognition accuracy, high line noise, and high time cost of feature recognition in traditional ceramic sculpture modeling feature recognition methods, this paper designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm. By constructing the sparse representation model of a traditional ceramic sculpture image, the posterior probability of a traditional ceramic sculpture image was determined and Gaussian distribution of pixels in the image was carried out to determine the distribution law of pixels in the image and the super-resolution reconstruction of the traditional ceramic sculpture image was realized. The feature of the line state of a traditional ceramic sculpture image was determined and classified by the kernel classification method. Finally, the machine learning algorithm red BP neural network is introduced to construct the traditional ceramic sculpture modeling feature recognition algorithm and the error threshold is constantly iterated to realize the traditional ceramic sculpture modeling feature recognition. The experimental results show that the recognition algorithm designed in this paper has high accuracy in identifying the traditional ceramic sculpture features, can effectively suppress the line noise, and has a short recognition time overhead, which has a certain feasibility.

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Wang, Qidong, Zhenya Wei, Jiaen Wang, Wuwei Chen, and Naihan Wang. "Curve recognition algorithm based on edge point curvature voting." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 4 (2019): 1006–19. http://dx.doi.org/10.1177/0954407019866975.

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In this paper, a new curve-lane recognition algorithm is proposed. The algorithm uses edge point curvature voting to determine the region of interest based on near-vision straight-lane information. First, information is detected in the near-vision area regarding the straight lines to the left and right of the current lane. Near-vision lane-line extraction includes lane image filtering, as well as edge detection of the region of interest below the vanishing line. The vanishing point is positioned by determining the position of the edge point and distribution of the direction angle. In addition, the straight line is extracted based on the position of the vanishing point. The straight lines that are constructed for the current lane in this way are selected and used as supplementation, in combination with the lane model. Next, the road curvature range isometry is divided into multiple subdivision regions. The near-vision lane straight-line curvature parameters extending from each edge point in the region of interest are computed by combining the straight-line near-vision lane information with the curve lane model in the pixel coordinate system. Subsequently, voting and counting are carried out for the curvature regions of each edge point to which the corresponding curvature computing values belong. Finally, the counting maximum from the corresponding curvature regions of the straight lines located to the left and right of the current lane are searched for, and the curvature region is converted, to obtain the lane line corresponding to the curvature parameter values. Experimental results indicate that the proposed curve-lane recognition algorithm can effectively detect the curve lanes of different curvatures. The results also indicate that the proposed curve detection method is highly accurate, and the algorithm is very robust in different environments.

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Ma, Feng Ying. "An Adaptive Pattern Recognition Algorithm in Coal Dust On-Line Measure." Advanced Materials Research 562-564 (August 2012): 1947–50. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1947.

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It is crucial to measure dust concentration precisely, but it normally varies with changes of working conditions. To increase precision and on-line performance of coal dust sensor, an adaptive pattern recognition algorithm was presented. The signals of unitary angular spectrums were chosen as the adaptive eigenvector for pattern recognition and pattern bank was established in advance. Furthermore, the ratio of the sum of inner signals to that of outer signals about the diffraction angular was considered as the eigenvector of subclass pattern classification. After classification, pattern could be recognized easily and rapidly. Subsequently, number of detailed patterns within different pattern groups was increased reasonably. The errors of total coal dust and respiring coal dust decline from 6% to 2.5% and from 9% to 3%, respectively. As a result, the precision of sensor achieves 95% during the measurement. It can be concluded that the adaptive pattern recognition algorithm is effective to improve the precision and real-time performance of coal dust sensor.

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Liu, Boyu, Hao Wang, Yongqiang Wang, Congling Zhou, and Lei Cai. "Lane Line Type Recognition Based on Improved YOLOv5." Applied Sciences 13, no. 18 (2023): 10537. http://dx.doi.org/10.3390/app131810537.

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The recognition of lane line type plays an important role in the perception of advanced driver assistance systems (ADAS). In actual vehicle driving on roads, there are a variety of lane line type and complex road conditions which present significant challenges to ADAS. To address this problem, this paper proposes an improved YOLOv5 method for recognising lane line type. This method can accurately and quickly identify the types of lane lines and can show good recognition results in harsh environments. The main strategy of this method includes the following steps: first, the FasterNet lightweight network is introduced into all the concentrated-comprehensive convolution (C3) modules in the network to accelerate the inference speed and reduce the number of parameters. Then, the efficient channel attention (ECA) mechanism is integrated into the backbone network to extract image feature information and improve the model’s detection accuracy. Finally, the sigmoid intersection over union (SIoU) loss function is used to replace the original generalised intersection over union (GIoU) loss function to further enhance the robustness of the model. Through experiments, the improved YOLOv5s algorithm achieves 95.1% of mAP@0.5 and 95.2 frame·s−1 of FPS, which can satisfy the demand of ADAS for accuracy and real-time performance. And the number of model parameters are only 6M, and the volume is only 11.7 MB, which will be easily embedded into ADAS and does not require huge computing power to support it. Meanwhile, the improved algorithms increase the accuracy and speed of YOLOv5m, YOLOv5l, and YOLOv5x models to different degrees. The appropriate model can be selected according to the actual situation. This plays a practical role in improving the safety of ADAS.

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Zhang, Su Wei, Wei Sun, and Si Yi Miao. "An Obstacle Recognition Method Based on Improved PSO-WNN for Deicing Robot on Voltage Transmission Line." Applied Mechanics and Materials 160 (March 2012): 53–58. http://dx.doi.org/10.4028/www.scientific.net/amm.160.53.

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An intelligent obstacle recognition method based on improved particle swarm optimization wavelet neural network (IPSO-WNN) for deicing robot on voltage transmission line is presented because traditional obstacle recognition methods often have errors due to structural constraints. Firstly, the improved PSO algorithm is proposed by introduce idea of probabilistic leap in simulated annealing. Secondly , the improved algorithm is used to optimize the parameters of the wavelet network. Finally, the trained IPSO-WNN is used to recognize and classify the wavelet moments of the obstacle edged images of robot. Experimental results show that compared with PSO-WNN this method have quicker convergence rate and higher classification precision and improve obviously recognition function of obstacle on transmission line.

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Sun, Feng Jie, Zhen Wang, and Jie Qing Fan. "The Recognition Research of Birds' Nest on Transmission Line Towers." Advanced Materials Research 651 (January 2013): 820–24. http://dx.doi.org/10.4028/www.scientific.net/amr.651.820.

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Because the birds’ behavior of building nests may influence the safe operation of transmission line tower, this paper puts forward a new kind of recognition scheme basing on digital image processing. After a careful analysis of different characteristics of the images with bird's nest and without nest, it is proposed that a large area connected region recognition algorithm. Firstly this algorithm transforms digital images into gray level. Secondly in order to reduce the influence of background on targets, iterative global threshold method is used. Then block statistics reduce the complexity of calculation and the potential impact of individual area pixel point on whole target recognition. Finally the judgment is finished by image geometric center. The scheme is simple to be used and calculation quantity is small, which is very suitable for transmission line monitoring and early warning.

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Chen, Muji. "Recognition and Localization of Target Images for Robot Vision Navigation Control." Journal of Robotics 2022 (March 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/8565913.

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This paper focuses on a visual navigation control system for mobile robots, recognizing target images and intelligent algorithms for the navigation system’s path tracking and localization techniques. This paper examines the recognition and localization of target images based on the visual navigation control of mobile robots. It proposes an efficient marking line method for recognizing and localization target images. Meanwhile, a fuzzy control method with smooth filtering and high efficiency is designed to improve the stability of robot operation, and the feasibility is verified in different scenarios. The corresponding image acquisition system is developed according to the characteristics of the experimental environment, and the acquired images are preprocessed to obtain corrected grayscale images. Then, target image recognition and linear fitting are performed to obtain target image positioning. The system calculates the angle and distance of the mobile robot, offsetting the target image in real time, adjusting the output signal, and controlling the mobile robot to realize path tracking. The comparison of sensor data and path tracking algorithm results during the experiment shows that the path tracking algorithm achieves good results with an angular deviation of ±1.5°. The application of RANSAC algorithm and improved Hough algorithm was analyzed in visual navigation control, and the two navigation line detection algorithms based on the image characteristics of the target image were improved in the optical detection area of the navigation line for the shortcomings of the two algorithms in visual navigation control, and the algorithms before and after the improvement were compared.

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Huang, Jianfeng, and Qiang Wan. "Smart grid line fault detection based on deep learning image recognition algorithm." International Journal of Low-Carbon Technologies 19 (2024): 2174–80. http://dx.doi.org/10.1093/ijlct/ctae164.

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Abstract Amid the rapid evolution of smart grids, stringent demands for reliability, safety, and efficiency have escalated for transmission lines. Nevertheless, given their extensive coverage and the complexity of their environments, traditional inspection techniques struggle to meet the rigorous standards of real-time monitoring and precision. This paper introduces a novel fault detection approach for smart grid transmission lines, leveraging advanced deep-learning image recognition algorithms. This method improves the YOLOv5 series models by combining the Convolution Attention Module (CBAM), Bidirectional Feature Pyramid Network (BiFPN), and MobileNet-V3 as the feature extraction network of YOLOv5 to achieve defect detection of hardware on transmission lines. CBAM improves the model’s sensitivity to tiny defects by focusing on key areas and features. BiFPN improves detection accuracy and robustness through efficient fusion of multi-scale features. Furthermore, the integration of MobileNet-V3 enhances both the efficiency and precision of feature extraction, ultimately elevating the overall model’s performance. Experimental results show that compared with the original YOLOv5 model, the improved algorithm can achieve 92.89% accuracy and 84.10% recall, which is better than other mainstream target detection algorithms. Especially in the detection of defects in small targets and complex backgrounds, the improved model shows stronger robustness and adaptability. This provides strong technical support for the efficient operation and maintenance of smart grids.

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Zhou, Yaoxiang, Hongdi Sun, Changlong Liu, Jiaming Zhang, Zhenbo Zhu, and Bin Tang. "Automatic Recognition Method of Broken Transmission Line Defect Image Based on Deep Transfer Learning." Journal of Physics: Conference Series 2189, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2189/1/012002.

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Abstract The material of ACSR in transmission line is prone to local damage, which leads to broken strand defect and reducing power consumption safety. Therefore, an automatic recognition method of broken strand defect image of transmission line based on deep transfer learning is designed to improve the automatic recognition effect of broken strand defect image. The multi-scale algorithm is used to enhance the image. In the feature extraction part of the depth transfer learning framework in the confusion domain, the multi-source domain transfer and dual flow fusion algorithm are used to extract the features of the enhanced image, and the Euclidean distance between the feature vector and the template image feature vector is used to correct the image features; using the corrected image feature training network propagated to the automatic defect recognition part and the domain classification part, the loss function and back propagation algorithm are used to reduce the loss of feature extraction and automatic defect recognition part, and the optimal results of automatic defect recognition and domain classification are obtained. The experimental results show that the method can enhance the image effectively with high definition. At different image angles, the recognition accuracy of this method is as high as 0.96, which has better automatic recognition effect of defect image.

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Dong, Yin Wen, Jing Xin An, Bing Cheng Yuan, and Zhao Ming Shi. "Arithmetic of Bridge Recognition Based on Straight-Line Characteristic." Applied Mechanics and Materials 263-266 (December 2012): 2586–91. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2586.

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Bridge recognition algorithm based on straight-line characteristic is proposed in order to automatically recognize bridge from aerial images, which includes the steps of edge detection, straight-line extraction, coarse location for bridge, accurate location for bridge. Meanwhile, realize the fast accurate location for bridge area by modified 8-neighborhood connectivity processing. The experiment result shows the reliability and efficiency of the method proposed in this article.

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Lazarov, A., and C. Minchev. "ISAR Image Recognition Algorithm and Neural Network Implementation." Cybernetics and Information Technologies 17, no. 4 (2017): 183–99. http://dx.doi.org/10.1515/cait-2017-0048.

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AbstractThe image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

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ALVAREZ, MIGUEL, and MARÍA-ELENA ALGORRI. "VECTORIZATION AND LINE DETECTION FOR AUTOMATIC IMAGE RECOGNITION." International Journal of Image and Graphics 11, no. 03 (2011): 439–70. http://dx.doi.org/10.1142/s0219467811004160.

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We propose an algorithm for creating line graphs from binary images. The algorithm consists of a vectorizer followed by a line detector that can handle a large variety of binary images and is tolerant to noise. The proposed algorithm can accurately extract higher-level geometry from the images lending itself well to automatic image recognition tasks. Our algorithm revisits the technique of image polygonization proposing a very robust variant based on subpixel resolution and the construction of directed paths along the center of the border pixels where each pixel can correspond to multiple nodes along one path. The algorithm has been used in the areas of chemical structure and musical score recognition and is available for testing at www.docnition.com. Extensive testing of the algorithm against commercial and noncommercial methods has been conducted with favorable results.

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GENOV, ROMAN, SHANTANU CHAKRABARTTY, and GERT CAUWENBERGHS. "SILICON SUPPORT VECTOR MACHINE WITH ON-LINE LEARNING." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 385–404. http://dx.doi.org/10.1142/s0218001403002472.

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Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin returned by the SVM classifier prior to assignment, subject to inequality constraints. The training scheme lends efficiently to dedicated SVM hardware for real-time pattern recognition, implemented using resources already provided for run-time operation. Performance gains are illustrated using the Kerneltron, a massively parallel mixed-signal VLSI processor for kernel-based real-time video recognition.

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Mao, Qianqian, Zheqiong Pan, Dongmei Si, and Xufeng Kong. "Research on Image Recognition Algorithm Technology for Power Line Business Audit." Journal of Physics: Conference Series 1650 (October 2020): 032002. http://dx.doi.org/10.1088/1742-6596/1650/3/032002.

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Pao, Derek C. W., M. C. Sun, and Murphy C. H. Lam. "An approximate string matching algorithm for on-line Chinese character recognition." Image and Vision Computing 15, no. 9 (1997): 695–703. http://dx.doi.org/10.1016/s0262-8856(96)01145-6.

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Subhi Mohammed, Iman, and Maher Khalaf Hussien. "Off-line handwritten signature recognition based on genetic algorithm and euclidean distance." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1238. http://dx.doi.org/10.11591/ijai.v12.i3.pp1238-1249.

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Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.

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Iman, Subhi Mohammed, and Khalaf Hussien Maher. "Off-line handwritten signature recognition based on genetic algorithm and Euclidean distance." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1238–49. https://doi.org/10.11591/ijai. v12.i3.pp1238-1249.

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Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean distance (ED) between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the Euclidean recognition system (ERS) was done. The application uses MATLAB.

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Wang, Chun Fang. "Fast Line Extraction Algorithm Based on Improved Hough Transformation." Advanced Materials Research 926-930 (May 2014): 3612–15. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3612.

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Automatic recognition of the line in the image is an important work in the field of machine vision and image processing. Focusing on the problem of the computational cost and large invalid sampling in the line extraction algorithm using standard Hough transform (HT). An improved HT algorithm is proposed to solve these problems. The parameters of the improved algorithm can be reduced to one and the accumulator is operated by setting the tolerance. Then the existence of linear is determined by seting the threshold. The experimental results show that the algorithm not only can effectively solve the problem of local maxima and improves the algorithm speed and reduces the storage space,but also the accuracy of line extraction is improved.

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Huang, Jin Xin, Ya Jin Li, Yang Jiang, et al. "The Online Pattern Recognition Method of SF₆ Gas Leakage Based on Image Recognition." Applied Mechanics and Materials 738-739 (March 2015): 682–85. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.682.

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Based on image processing of SF6 gas leakage on-line pattern recognition method, this paper achieves gas leakage feature extracting, on-line identification of gas leakage and leakage points, SF6 gas leakage can be on-line automatic identification. The simulation results show the feasibility of the algorithm. Compared with the traditional method, paper provides a more intuitive discrimination basis for field staff , as well as for the depth of the late testing data mining provides a research way of thinking

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Dong, Yin Wen, Luan Wan, Zhao Ming Shi, and Jing Xin An. "An Anhydrous Bridge Recognition Algorithm in Aerial Image." Advanced Materials Research 734-737 (August 2013): 3079–84. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.3079.

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Aiming at anhydrous bridge automatically identification in aerial images, an anhydrous bridge recognition algorithm based on the geometric characteristics is proposed. Firstly, the original image is do threshold segmentation to get binary image. Secondly, binary image is do morphological processed to get bridge area enhanced image and bridge area corrosion image, and these two bridge area are subtracted to extract suspected bridge area based on bridge rectangle feature. Finally, bridge regional area is positioned according to the straight-line characteristics of the bridge. Experimental results show the proposed algorithm can accurately identify the anhydrous bridge effectively. Key words: aerial image; anhydrous bridges identification; edge detection ; straight line extraction ; geometric features

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Ulgen, Figen, Norio Akamatsu, and Takenori Iwasa. "The Hypercube Separation algorithm: A fast and efficient algorithm for on-line handwritten character recognition." Applied Intelligence 6, no. 2 (1996): 101–16. http://dx.doi.org/10.1007/bf00117811.

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Cheng, Xintian. "The Framework of Passable Region Recognition Based on Vanish-Line." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 1 (2019): 97–101. http://dx.doi.org/10.20965/jaciii.2019.p0097.

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For the defect of the traditional vanishing point detection algorithm that is invalid in unstructured environment, a novel vanishing detection algorithm based on Dynamic Template Matching (DTM) is proposed. And a framework of access area recognition is put forward according to the vanishing point line. First, a series of lines are selected from the image in the form of the scanning at the same interval and then calculate the between each line and the previous one. The horizontal position of vanish point is that of the line with the minimum normalized correlation value in all scanning line. Second, a new image is constructed by getting rid of the part above of the viewpoint line, and be divided into several subimages without overlap to extract the multi features. The end, a train set is constructed based on the assumption of no deviation of the vehicle and the test set is classified by multi-kernel learning (MKL) method to obtain passable area. In addition, according to the need of intelligent vehicles during working, a weight-accuracy is delimited by assigning the different weights to the near areas and far areas. This kind of accuracy is more significative than the original one. In the experiments on various environments image sets, the proposed method exhibits favorable performances compared to the other methods.

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Ptak, Roman, Bartosz Żygadło, and Olgierd Unold. "Projection–Based Text Line Segmentation with a Variable Threshold." International Journal of Applied Mathematics and Computer Science 27, no. 1 (2017): 195–206. http://dx.doi.org/10.1515/amcs-2017-0014.

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Abstract Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.

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CHEN, LIANG-HUA, and JIING-YUH WANG. "RECOGNITION OF NUMERAL STRINGS ON MAPS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 03 (1999): 319–37. http://dx.doi.org/10.1142/s0218001499000185.

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This paper presents a complete procedure for the extraction and recognition of hand-printed numeral strings on maps. The extraction algorithm can extract individual characters from a map even if the characters touch each other or touch with graphical line. The feature-based recognition algorithm can recognize numeral characters of any size, position and orientation. Our features for discrimination are simple and easily detectable. Experimental results on utility and cadastral maps have shown that the proposed technique is effective in the automatic data capture of geographic information systems.

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Kherallah, M., F. Bouri, and A. M. Alimi. "On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm." Engineering Applications of Artificial Intelligence 22, no. 1 (2009): 153–70. http://dx.doi.org/10.1016/j.engappai.2008.05.010.

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Gao, Jun, Xin Ye, Zhi Jing Zhang, Yong Long Tang, and Xin Jin. "A Vision Detection Algorithm for LIGA Part Assembly." Applied Mechanics and Materials 325-326 (June 2013): 1271–75. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1271.

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This paper proposes a vision detection algorithm to acquire LIGA part’s edges based on an in-house multi-DOF manipulator for LIGA part assembly. Feature recognition based on maximum information entropy is proposed to solve the problem that high precision edge recognition under backlight source. In order to further improve vision recognition accuracy, edge feature recognition algorithm based on symmetrical edge is proposed to recognize the center line of the symmetrical parts when the quality of the image is poor.

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KAVIANPOUR, A., and N. BAGHERZADEH. "PARALLEL ALGORITHMS FOR LINE DETECTION ON A PYRAMID ARCHITECTURE." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 337–49. http://dx.doi.org/10.1142/s0218001494000164.

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This paper considers the problem of detecting lines in images using a pyramid architecture. The approach is based on the Hough Transform calculation. A pyramid architecture of size n is a fine-grain architecture with a mesh base of size [Formula: see text] processors each holding a single pixel of the image. The pyramid operates in an SIMD mode. Two algorithms for computing the Hough Transform are explained. The first algorithm initially uses different angles, θj’s, and its complexity is O(k+log n) with O(m) storage requirement. The second algorithm computes the Hough Transform in a pipeline fashion for each angle θj at a time. This method produces results in O(k * log n) time with O(1) storage, where k is the number of θj angles, m is the number of ρi normal distances from the origin, and n is the number of pixels. A simulation program is also described.

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Wang, Qingyan, Zhen Zhang, Qingguo Chen, Junping Zhang, and Shouqiang Kang. "Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny." Sensors 24, no. 2 (2024): 565. http://dx.doi.org/10.3390/s24020565.

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Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.

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Ulgen, Figen, Andrew Flavell, and Norio Akamatsu. "On-Line Shape recognition with incremental training using binary synaptic weights algorithm." Applied Intelligence 6, no. 3 (1996): 225–40. http://dx.doi.org/10.1007/bf00126628.

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Liu, Jianzhuang, W. K. Cham, and Michael M. Y. Chang. "On-Line Chinese Character Recognition by Incorporating Human Knowledge." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 01 (1997): 13–29. http://dx.doi.org/10.1142/s0218488597000038.

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A stroke order and number free method for on-line recognition of Chinese characters is proposed. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). The ARGs of the model base are built according to the human knowledge of the segment relation structure of Chinese characters. For the recognition purpose, an optimal matching measure between two ARGs is defined, and the graph matching is formulated as a search problem of finding the minimum cost path in a state space tree, using the A * algorithm. Moreover, to reduce the search time of the A *, besides a heuristic estimate, a novel strategy is employed which again uses the human knowledge of the segment position structure of Chinese characters to prune the tree. Our experimental results demonstrate the efficiency of the proposed method.

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Guo, Jiaqi. "Image Identification Algorithm of Deep Compensation Transformation Matrix based on Main Component Feature Dimensionality Reduction." Journal of Imaging Science and Technology 64, no. 4 (2020): 40408–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.4.040408.

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Abstract In order to reconstruct and identify three-dimensional (3D) images, an image identification algorithm based on a deep learning compensation transformation matrix of main component feature dimensionality reduction is proposed, including line matching with point matching as the base, 3D reconstruction of point and line integration, parallelization automatic differentiation applied to bundle adjustment, parallelization positive definite matrix system solution applied to bundle adjustment, and an improved classifier based on a deep compensation transformation matrix. Based on the INRIA database, the performance and reconstruction effect of the algorithm are verified. The accuracy rate and success rate are compared with L1APG, VTD, CT, MT, etc. The results show that random transformation and re-sampling of samples during training can improve the performance of the classifier prediction algorithm under the condition that the training time is short. The reconstructed image obtained by the algorithm described in this study has a low correlation with the original image, with high number of pixels change rate (NPCR) and unified average changing intensity (UACI) values and low peak signal to noise ratio (PSNR) values. Image reconstruction effect is better with image capacity advantage. Compared with other algorithms, the proposed algorithm has certain advantages in accuracy and success rate with stable performance and good robustness. Therefore, it can be concluded that image recognition based on the dimension reduction of principal component features provides good recognition effect, which is of guiding significance for research in the image recognition field.

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Wu, Xi-Bao, Si-Chuan Lv, Xiao-Hao Wang, et al. "Discontinuous Track Recognition System Based on PolyLaneNet for Darwin-op2 Robot." Computational Intelligence and Neuroscience 2022 (February 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/5431886.

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This paper proposes and demonstrates a single-line discontinuous track recognition system by associating the track recognition problem of a humanoid robot with the lane detection problem. The proposal enables the robot to achieve stable running on the single-line discontinuous track. The system consists of two parts: the robot end and the graphics computing end. The robot end is responsible for collecting track information and the graphics computing end is responsible for high-performance computing. These two parts use the TCP for communication. The graphics computing side uses PolyLaneNet lane detection algorithm to train the track image captured from the first perspective of the darwin-op2 robot as the data set. In the inference, the robot end sends the collected tracking images to the graphics calculation end and uses the graphics processor to accelerate the calculation. After obtaining the motion vector, it is transmitted back to the robot end. The robot end parses the motion vector to obtain the motion information of the robot so that the robot can achieve stable running on the single-line discontinuous track. The proposed system realizes the direct recognition of the first perspective image of the robot and avoids the problems of poor stability, inability of identifying curves and discontinuous lines, and other problems in the traditional line detection method. At the same time, this system adopts the method of cooperative work between the PC side and the robot by deploying the algorithm with high computational requirements on the PC side. The data transmission is carried out by stable TCP communication, which makes it possible for the robot equipped with weak computational controllers to use deep-learning-related algorithms. It also provides ideas and solutions for deploying deep-learning-related algorithms on similar low computational robots.

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Bengio, Yoshua, Yann LeCun, Craig Nohl, and Chris Burges. "LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition." Neural Computation 7, no. 6 (1995): 1289–303. http://dx.doi.org/10.1162/neco.1995.7.6.1289.

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We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the EM algorithm. Words are then coded into low resolution "annotated images" where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network that can be spatially replicated. From the network output, a hidden Markov model produces word scores. The entire system is globally trained to minimize word-level errors.

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Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." International Journal of Computer Vision and Image Processing 1, no. 2 (2011): 58–77. http://dx.doi.org/10.4018/ijcvip.2011040105.

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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.

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Li, Lian Huan. "Research on Character Segmentation Method in Image Text Recognition." Advanced Materials Research 546-547 (July 2012): 1345–50. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1345.

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Character Segmentation is the key step for image text recognition. This paper presents a text tilt correction algorithm using tracked characteristics rectangle contour to extract angle, using line scan method based on the number of transitions to determine the character on the bottom. In order to meet the requirements of real-time and reliability, takes improved secondary single-character segmentation algorithm based on vertical projection method.

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Wen, Qiao, and Rui-Guang Zhu. "Automatic Generation of 3D Building Models Based on Line Segment Vectorization." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–16. http://dx.doi.org/10.1155/2020/8360706.

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Operations and maintenance (O&M) management for existing buildings is of high importance since it consumes the most cost during buildings’ lifecycle. Its effectiveness could be significantly improved through the systematic use of building information modeling (BIM). However, BIM relies on full-fledged digital models, which, for most buildings, are not available. This paper introduces a recognition algorithm aiming at the automatic generation of 3D building models from 2D drawings. The algorithm is able to generate separated wall segment 3D models with their topology relations. The algorithm is implemented and tested by several real projects. The results are very promising and show that the proposed algorithm could be a key component of future digital toolkits for O&M management.

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Hong, Chen, Gareth Loudon, Yimin Wu, and Ruslana Zitserman. "Segmentation and Recognition of Continuous Handwriting Chinese Text." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (1998): 223–32. http://dx.doi.org/10.1142/s0218001498000154.

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This article introduces the basic segmentation problems in Chinese handwriting and also several prior work to solve these problems. A new segmentation method is proposed, which is applicable to both on-line and off-line systems for free-format handwritten Chinese character sentences. This method performs basic segmentation and fine segmentation based on the varying spacing thresholds and the minimum variance criteria. The five most probable ways of segmentation are derived from this stage and all the possible segments are extracted and recognized. A lattice is created from all the segments and searched using a viterbi based algorithm to find the most likely character sequence. The algorithm presented in this paper provides large flexibility and robustness to handle free-format continuous Chinese handwriting and is a promising solution for a natural and fast Chinese pen input system. The character accuracy is 85.0% for on-line and 77.4% for the off-line test data.

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Yang, Yin Xian, Li Zhao, Cai Rong Zou, and Yin Xian Yang. "Staff Line Removal Algorithm and Research Based on Run-Length Graph Slice and Topological Structure of Music." Advanced Materials Research 760-762 (September 2013): 1429–33. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1429.

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Staff line removal is a key step before segmentation and recognition of music image and plays an important role in OMR (Optical Music Recognition) research, the result of staff line removal directly influences the performance and function of the whole OMR system. However, over-removal and under-removal often occurs in the processing and leads to the low efficiency of music recognition rate. So, in order to solve the arduous problem, an approach based on run-length graph slice and topological structure of music is put forward by careful analysis of staff line and music notation structure. Experience results show the validity and practicality of the presented algorithm fast and effectively.

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