Gesture objects detection and tracking for virtual text entry keyboard interface

GCR‐Net: A deep learning‐based bare hand detection and gesticulated character recognition system for human‐computer interaction

“…In gesticulated character, we can provide preliminary information to the model during the gesticulation. In addition, it is reported by the researchers 3 that the complexity increased with the higher number of classes. Thus, the separated networks for each set of characters, that is, digits, uppercase alphabets, lowercase alphabets, operations, and special characters, may provide more accurate performance.…”

“…Most researchers 2,3,5‐11 experimented and proved their claim in a controlled environment for bare‐hand detection for detecting the bare hand. Initially, researchers 6 used skin‐color information and Viola Jones's algorithm to locate the skin color pixels for bare‐hand detection.…”

“…These models can obtain more robust features for specific applications. Applying the DCNN model with the sliding window 15 was computationally high; therefore, the researchers 3 proposed an ROI‐based DCNN model. In this, movement and skin color information was used to obtain the ROI of the bare hand.…”

“…Character recognition has gained much attention in recent years due to its potential applications 1‐10 like document analysis, language translation, number plate recognition, text‐to‐speech converter, air‐writing, and virtual text entry systems. Particularly, gesticulated character recognition is still needed to be explored further.…”

“…Particularly, gesticulated character recognition is still needed to be explored further. Few researchers 2‐5 worked to design gesticulated characters recognition system due to its complexity in bare‐hand detection and gesticulated trajectory recognition. The initial stage of these systems is bare‐hand detection, which should be implemented precisely.…”