Context Based Out of Vocabulary

Context-based out-of-vocabulary (OOV) word handling addresses the challenge of processing words unseen during model training, crucial for robust natural language processing and computer vision systems. Current research focuses on improving OOV word recognition through techniques like pseudo-data generation, loss function adjustments prioritizing OOV words, and incorporating contextual information via attention mechanisms and transformer-based architectures. These advancements aim to enhance the accuracy and adaptability of models to real-world scenarios where novel words frequently appear, impacting applications such as speech recognition, scene text recognition, and novel object captioning.