Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Machine Learning
A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Australian sign language recognition
Machine Vision and Applications
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
A Chinese sign language recognition system based on SOFM/SRN/HMM
Pattern Recognition
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Vision-Based recognition of fingerspelled acronyms using hierarchical temporal memory
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Fingerspelling is used in sign language to spell out names of people and places for which there is no sign or for which the sign is not known. In this work we describe a Turkish fingerspelling recognition system that recognizes all 29 letters of the Turkish alphabet. A single representative frame is extracted from the sign video, since that frame is enough for recognition purposes of the letters mentioned. Processing a single frame, instead of the whole video, increases speed considerably. The skin regions in the representative frame are extracted by color segmentation in YCrCb space before clearing noise regions by morphological opening. A novel fast alignment method that uses the angle of orientation between the axis of least inertia and y axis is applied to hand regions. This method compensates small orientation differences but increases big ones. This is desirable when differentiating the fingerspelling signs, some of which are close in shape but different in orientation. Also the use of minimum bounding square is advised, which helps in resizing without breaking the alignment. Binary values of this minimum bounding square are directly used as feature values, and that allowed experimenting with different classification schemes. Features like mean radial distance and circularity are also used for increasing success rate. Classifiers like kNN, SVM, Naïve Bayes, and RBF Network are experimented with, and 1NN and SVM are found to be the best two of them. The video database was created by 3 different signers, a set of 290 training videos, and a separate set of 174 testing videos are used in experiments. The best classifiers 1NN and SVM achieved a success rate of 99.43% and 98.83% respectively.