Multilingual-Word-Script Classification in Text Video Frames - Sunil, C; Chethan, HK; Raghunandan, KS; G, Hemantha Kumar (International Journal of Computer Sciences and Engineering, 2019)
Nowadays, achieving good results for the text classification of the multilingual scripts in arbitrary images in the videos is the most challenging task for the researchers. Most of the people depends on the internet and the digital world that makes difficult task to understand the multilingual script in various domain. Motivated from this, we proposed a text classification model for multilingual-word-scripts in video frames extracted from the videos which contains South Indian Multilingual Scripts namely, English, Tamil, Kannada, Malayalam and Telugu. Six-layer convolution neural network model has been used to classify the text to their respective classes. In this work we have castoff 600 word images from each script and total of 3000 word images that is extracted as the word images from the video frames for our experimentation. Our proposed model is proficient in accomplishing decent classification results when compared to existing conventional methods such as KNN and SVM classifier.