The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Dit-yan Yeung - One of the best experts on this subject based on the ideXlab platform.
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elastic structural matching for online handwritten Alphanumeric Character recognition
International Conference on Pattern Recognition, 1998Co-Authors: Kamfai Chan, Dit-yan YeungAbstract:We propose a simple yet robust structural approach for recognizing online handwriting. Our approach is designed to achieve reasonable speed, fairly high accuracy and sufficient tolerance to variations. Experimental results show that the recognition rates are 98.60% for digits, 98.49% for uppercase letters, 97.44% for lowercase letters, and 97.40% for the combined set. When the rejected cases are excluded from the calculation, the rates can be increased to 99.93%, 99.53%, 98.55% and 98.07%, respectively. On the average, the recognition speed is about 7.5 Characters per second running in Prolog on a Sun SPARC 10 Unix workstation and the memory requirement is reasonably low.
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Elastic structural matching for on-line handwritten Alphanumeric Character recognition
1998Co-Authors: Kamfai Chan, Dit-yan YeungAbstract:In this paper, we will propose a simple yet robust structural approach for recognizing on-line handwriting. Our approach is designed to achieve reasonable speed, fairly high accuracy and sufficient tolerance to variations. Experimental results show that the recognition rates are 98.60% for digits, 98.49% for uppercase letters, 97.44% for lowercase letters, and 97.40% for the combined set. When the rejected cases are excluded from the calculation, the rates can be increased to 99.93%, 99.53%, 98.55% and 98.07%, respectively. On the average, the recognition speed is about 7.5 Characters per second running in Prolog on a Sun SPARC 10 Unix workstation and the memory requirement is reasonably low.
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On-line handwritten Alphanumeric Character recognition using dominant points in strokes
Pattern Recognition, 1997Co-Authors: Dit-yan YeungAbstract:Abstract All Alphanumeric Characters can be written in certain styles with strokes of different shapes and positions. An on-line handwritten Character written on a digitizing tablet is represented as a sequence of strokes, which are the loci of the pen tip from its pen-down to pen-up positions. In this paper, we present an approach to on-line handwritten Alphanumeric Character recognition based on sequential handwriting signals. In our approach, an on-line handwritten Character is Characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information of the dominant points is used for Character pre-classification and the positional information is used for fine classification. Both pre-classification and fine classification are based on dynamic programming matching using the idea of band-limited time warping. These techniques are elastic, in that they can tolerate local variation and deformation. The issue of reference (or template) set evolution is also addressed. A recognition experiment has been conducted with 62 Character classes (0–9, A–Z, a–z) of different writing styles (Italian manuscript style and some other styles) and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 s per Character on a 486 50 MHz personal computer.
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on line handwritten Alphanumeric Character recognition using feature sequences
Lecture Notes in Computer Science, 1995Co-Authors: Dit-yan YeungAbstract:In this paper we present an approach in which an on-line handwritten Character is Characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information is used for Character preclassification and the positional information is used for fine classification. Doth preclassification and fine classification are based on dynamic programming matching. A recognition experiment has been conducted with 62 Character classes of different writing styles and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 second per Character on a 486 50MHz personal computer.
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ICSC - On-Line Handwritten Alphanumeric Character Recognition Using Feature Sequences
Lecture Notes in Computer Science, 1995Co-Authors: Dit-yan YeungAbstract:In this paper we present an approach in which an on-line handwritten Character is Characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information is used for Character preclassification and the positional information is used for fine classification. Doth preclassification and fine classification are based on dynamic programming matching. A recognition experiment has been conducted with 62 Character classes of different writing styles and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 second per Character on a 486 50MHz personal computer.
Bingyu Chen - One of the best experts on this subject based on the ideXlab platform.
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edgevib effective Alphanumeric Character output using a wrist worn tactile display
User Interface Software and Technology, 2016Co-Authors: Yichi Liao, Yiling Chen, Ronghao Liang, Liwei Chan, Bingyu ChenAbstract:This paper presents EdgeVib, a system of spatiotemporal vibration patterns for delivering Alphanumeric Characters on wrist-worn vibrotactile displays. We first investigated spatiotemporal pattern delivery through a watch-back tactile display by performing a series of user studies. The results reveal that employing a 2×2 vibrotactile array is more effective than employing a 3×3 one, because the lower-resolution array creates clearer tactile sensations in less time consumption. We then deployed EdgeWrite patterns on a 2×2 vibrotactile array to determine any difficulties of delivering Alphanumerical Characters, and then modified the unistroke patterns into multistroke EdgeVib ones on the basis of the findings. The results of a 24-participant user study reveal that the recognition rates of the modified multistroke patterns were significantly higher than the original unistroke ones in both alphabet (85.9% vs. 70.7%) and digits (88.6% vs. 78.5%) delivery, and a further study indicated that the techniques can be generalized to deliver two-Character compound messages with recognition rates higher than 83.3%. The guidelines derived from our study can be used for designing watch-back tactile displays for Alphanumeric Character output.
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UIST - EdgeVib: Effective Alphanumeric Character Output Using a Wrist-Worn Tactile Display
Proceedings of the 29th Annual Symposium on User Interface Software and Technology, 2016Co-Authors: Yichi Liao, Yiling Chen, Ronghao Liang, Liwei Chan, Bingyu ChenAbstract:This paper presents EdgeVib, a system of spatiotemporal vibration patterns for delivering Alphanumeric Characters on wrist-worn vibrotactile displays. We first investigated spatiotemporal pattern delivery through a watch-back tactile display by performing a series of user studies. The results reveal that employing a 2×2 vibrotactile array is more effective than employing a 3×3 one, because the lower-resolution array creates clearer tactile sensations in less time consumption. We then deployed EdgeWrite patterns on a 2×2 vibrotactile array to determine any difficulties of delivering Alphanumerical Characters, and then modified the unistroke patterns into multistroke EdgeVib ones on the basis of the findings. The results of a 24-participant user study reveal that the recognition rates of the modified multistroke patterns were significantly higher than the original unistroke ones in both alphabet (85.9% vs. 70.7%) and digits (88.6% vs. 78.5%) delivery, and a further study indicated that the techniques can be generalized to deliver two-Character compound messages with recognition rates higher than 83.3%. The guidelines derived from our study can be used for designing watch-back tactile displays for Alphanumeric Character output.
Serguei Levachkine - One of the best experts on this subject based on the ideXlab platform.
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Text/graphics separation and recognition in raster-scanned color cartographic maps
Lecture Notes in Computer Science, 2004Co-Authors: Aurelio Velázquez, Serguei LevachkineAbstract:A method to separate and recognize the touching/overlapping Alphanumeric Characters is proposed. The Characters are processed in raster-scanned color cartographic maps. The map is segmented first to extract all text strings including those that are touching other symbols, strokes and Characters. Second, OCR-based recognition with Artificial Neural Networks (ANN) is applied to define the coordinates, size and orientation of Alphanumeric Character strings in each case presented in the map. Third, four straight lines or a number of curves computed as a function of primarily recognized by ANN Characters are extrapolated to separate those symbols that are attached. Finally, the separated Characters input into ANN again to be finally identified. Results showed high method's rendering in the context of raster-to-vector conversion of color cartographic images.
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GREC - Text/Graphics Separation and Recognition in Raster-Scanned Color Cartographic Maps
Graphics Recognition. Recent Advances and Perspectives, 2004Co-Authors: Aurelio Velázquez, Serguei LevachkineAbstract:A method to separate and recognize the touching/overlapping Alphanumeric Characters is proposed. The Characters are processed in raster-scanned color cartographic maps. The map is segmented first to extract all text strings including those that are touching other symbols, strokes and Characters. Second, OCR-based recognition with Artificial Neural Networks (ANN) is applied to define the coordinates, size and orientation of Alphanumeric Character strings in each case presented in the map. Third, four straight lines or a number of “curves” computed as a function of primarily recognized by ANN Characters are extrapolated to separate those symbols that are attached. Finally, the separated Characters input into ANN again to be finally identified. Results showed high method’s rendering in the context of raster-to-vector conversion of color cartographic images.
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Color image segmentation using false colors and its applications to geo-images treatment: Alphanumeric Character recognition
IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH, 1Co-Authors: Serguei Levachkine, A. Velazquez, V. AlexandrovAbstract:In this work an approach is proposed to segment the Alphanumeric Characters that present in a cartographic color map, using the model-of color RGB. Corresponding raster image is obtained by means of scanning, following to the strategies proposed in S. Levachkine et al. (2000). Our approach does not require a preprocessing of the images, because they only maintain the pixels that really belong to the Characters we wish to segment, eliminating all those pixels that are not of interest, produced by a noise or obtained due to erroneous selection of scan parameters (for example, scan resolution), etc. The following identification of the Alphanumeric Characters supports by a set of neural network and the dictionaries with the Character names related to the map or particular application that has previously been prepared.
Yichi Liao - One of the best experts on this subject based on the ideXlab platform.
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edgevib effective Alphanumeric Character output using a wrist worn tactile display
User Interface Software and Technology, 2016Co-Authors: Yichi Liao, Yiling Chen, Ronghao Liang, Liwei Chan, Bingyu ChenAbstract:This paper presents EdgeVib, a system of spatiotemporal vibration patterns for delivering Alphanumeric Characters on wrist-worn vibrotactile displays. We first investigated spatiotemporal pattern delivery through a watch-back tactile display by performing a series of user studies. The results reveal that employing a 2×2 vibrotactile array is more effective than employing a 3×3 one, because the lower-resolution array creates clearer tactile sensations in less time consumption. We then deployed EdgeWrite patterns on a 2×2 vibrotactile array to determine any difficulties of delivering Alphanumerical Characters, and then modified the unistroke patterns into multistroke EdgeVib ones on the basis of the findings. The results of a 24-participant user study reveal that the recognition rates of the modified multistroke patterns were significantly higher than the original unistroke ones in both alphabet (85.9% vs. 70.7%) and digits (88.6% vs. 78.5%) delivery, and a further study indicated that the techniques can be generalized to deliver two-Character compound messages with recognition rates higher than 83.3%. The guidelines derived from our study can be used for designing watch-back tactile displays for Alphanumeric Character output.
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UIST - EdgeVib: Effective Alphanumeric Character Output Using a Wrist-Worn Tactile Display
Proceedings of the 29th Annual Symposium on User Interface Software and Technology, 2016Co-Authors: Yichi Liao, Yiling Chen, Ronghao Liang, Liwei Chan, Bingyu ChenAbstract:This paper presents EdgeVib, a system of spatiotemporal vibration patterns for delivering Alphanumeric Characters on wrist-worn vibrotactile displays. We first investigated spatiotemporal pattern delivery through a watch-back tactile display by performing a series of user studies. The results reveal that employing a 2×2 vibrotactile array is more effective than employing a 3×3 one, because the lower-resolution array creates clearer tactile sensations in less time consumption. We then deployed EdgeWrite patterns on a 2×2 vibrotactile array to determine any difficulties of delivering Alphanumerical Characters, and then modified the unistroke patterns into multistroke EdgeVib ones on the basis of the findings. The results of a 24-participant user study reveal that the recognition rates of the modified multistroke patterns were significantly higher than the original unistroke ones in both alphabet (85.9% vs. 70.7%) and digits (88.6% vs. 78.5%) delivery, and a further study indicated that the techniques can be generalized to deliver two-Character compound messages with recognition rates higher than 83.3%. The guidelines derived from our study can be used for designing watch-back tactile displays for Alphanumeric Character output.
Kaushik Deb - One of the best experts on this subject based on the ideXlab platform.
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n eural n etwork based english Alphanumeric Character recognition
International Journal of Computer Science Engineering and Applications, 2012Co-Authors: Fazlul Kader, Kaushik DebAbstract:Propose a neural-network based size and color invariant Character recognition system using feed -forward neural network. Our feed-forward network has two layers. One is input layer and another is output layer. The whole recognition process is divided into four basic steps such as pre -processing, normalization, network establishment and recognition. Pre -processing involves digitization, noise remova l and boundary detection. After boundary detection, the input Character matrix is normalized into 12×8 matrix for size invariant recognition and fed into the proposed network which consists of 96 input and 36 output neurons. Then we trained our network byproposed training algorithm in a supervised manner and established the network by adjusting weights. Finally, we have tested our network by more than 20 samples per Character on average and give 99.99% accuracy only for numeric digits (0~9), 98% accuracy o nly for letters (A~Z) and more than 94% accuracy for Alphanumeric Characters by considering inter -class similarity measurement.
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vehicle license plate detection method based on sliding concentric windows and histogram
Journal of Computers, 2009Co-Authors: Kaushik Deb, Hyunuk ChaeAbstract:Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP Alphanumeric Character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP Alphanumeric Character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.
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parallelogram and histogram based vehicle license plate detection
International Conference on Smart Manufacturing Application, 2008Co-Authors: Kaushik Deb, Hyunuk ChaeAbstract:This paper describes a new approach to analyze road images which often contain vehicles and extract license plate (LP) from natural properties by finding vertical and horizontal edges from vehicle region. The proposed technique consists of three main modules: (a) segmentation technique named as sliding concentric windows (SCW) on the basis of a novel adaptive image for detecting candidate region, (b) refining by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively and (c) finally, verify and detect VLP region which contains predetermined LP Alphanumeric Character by using position histogram. In the proposed method, input vehicle images are converted into gray images. After then the candidate regions are found by sliding concentric windows. We detect vehicle license plates (VLP) region which contains predetermined LP color by using HSI color model and LP Alphanumeric Character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination and varied weather. Experimental results also show that the distance from the vehicle varied according to the camera setup.