Sign language number recognition system lays down foundation for handshape recognition which addresses real and current problems in signing in the deaf community and leads to practical applications. The input for the sign language number recognition system is 5000 Filipino Sign Language number video file with 640 x 480 pixels frame size and 15 frame/second. The color-coded gloves uses less color compared with other color-coded gloves in the existing research. The system extracts important features from the video using multi-color tracking algorithm which is faster than existing color tracking algorithm because it did not use recursive technique. Next, the system learns and recognizes the Filipino Sign Language number in training and testing phase using Hidden Markov Model. The system uses Hidden Markov Model (HMM) for training and testing phase. The feature extraction could track 92.3% of all objects. The recognizer also could recognize Filipino sign language number with 85.52% average accuracy.

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Abstract. In the education world, the number of web-based system is growing substantially in the past few years. Many web-based teaching tools has been developed to help students expand his knowledge. This paper presents a web-based laboratory to augment learning experiences in the field of robotics. The designed laboratory consists of a robotic arm with laser pointer, projection platform and web-based interface. The laboratory is used to learn about inverse kinematics and forward kinematics problems. Students enter the input parameters through web interface, server calculate output parameters and sent it to the robot. The web interface displays the movement result and turn on the laser pointer to the projection field. Preliminary testing concerning interface usability, access control mechanism and command queueing shows that the system working as expected.