This academic work has the main objective to develop a system for automatic recognition of an English playing card located on a table using computer vision techniques for capturing, preprocessing, and segmenting the image independently of the orientation and the depression angle. The algorithm used as a classifier to recognize the card is k-nearest neighbor (kNN). At training stage, a set based on a list of alphanumeric characters was used. The result of the classification was sent to an audio output using a converter from text to voice. This algorithm was implemented in an embedded system Raspberry Pi 3 under the operative system Raspbian Jessie. The system developed has an accuracy of 95% and an average wait-response of 5 seconds taking into account the audio playing.
|Translated title of the contribution||Identificación automática de una tarjeta de juego a través de Knn Uso de una Raspberry Pi 3|
|Original language||English (US)|
|State||Published - Nov 2016|
|Event||International Conference on Information Systems and Computer Science - Quito, Ecuador|
Duration: 24 Nov 2016 → 26 Nov 2016
|Conference||International Conference on Information Systems and Computer Science|
|Abbreviated title||INCISCOS 2016|
|Period||24/11/16 → 26/11/16|
Tufiño Cardenas, R. E., & Ortega Martinez, H. R. (2016). Automatic Identification Of A Playing Card Through Knn Using A Raspberry Pi 3. Paper presented at International Conference on Information Systems and Computer Science, Quito, Ecuador.