People often switch languages in conversations or written communication in order to communicate thoughts on social media platforms. The languages in texts of this type, also known as code-mixed texts, can be mixed at the sentence, word, or even sub-word level. In this paper, we address the problem of identifying language at the word level in code-mixed texts using a sequence of characters and word embedding. We feed machine learning and deep neural networks with a range of character-based and word-based text features as input. The data for this experiment was created by combining YouTube video comments from code-mixed Kannada and English (Kn-En) texts. The texts were pre-processed, split into words, and categorized as ‘Kannada’, ‘English’, ‘Mixed-Language’, ‘Name’, ‘Location’, and ‘Other’. The proposed techniques were able to learn from these features and were able to effectively identify the language of the words in the dataset. The proposed CK-Keras model with pre-trained Word2Vec embedding was our best-performing system, as it outperformed other methods when evaluated by the F1 scores.