In this thesis, two experimental text entry methods for mobile touchscreen devices are modeled and implemented. Both text entry methods are based on the grapheme shaping, and differ primarily by the type of used touch gestures. The KDI method involves discrete touch gestures for entering a particular letter, wherein the user must activate a certain touch-point sequence that represents an appropriate grapheme in the touch-interactive network. KDI encompasses two operation modalities: Standard – which requires only the exact combination of touchpoints in a given sequence, and Forced – which additionally assumes correct order in activating such sequence, according to the standard writing rules. The second text entry method – KCI – is based on continuous touch gestures, allowing the user to directly draw the grapheme, which is then classified using a neural network model. The classifier used in KCI virtual keyboard is derived from the MobileNet convolution neural network trained on the EMNIST data set, using the TensorFlow programming framework. All mentioned text entry methods were comparatively evaluated in a controlled experiment with twenty users. Obtained results have shown that text entry is significantly more efficient with KCI method, versus both versions of the KDI (Standard and Forced). Shaping graphemes using continuous touch gestures was expectedly shown to be a more intuitive approach. On the other hand, the concept utilizing discrete touch gestures caused greater mental effort and higher levels of frustration, primarily due to the need for learning the appropriate grapheme sequences.