BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%.
Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.
Deep Learning-Based Approach for Sign Language Gesture Recognition With Efficient Hand Gesture Representation