"Object Identification and Captioning Using Machine Learning and Deep Learning Techniques (LSTM)"

Deep learning and Machine learning are the most progressive technologies in this era.
Artificial intelligence is now compared with the human mind and in some field AI doing a great
job than humans. Automatically describing the content of images using natural languages is a
fundamental and challenging task. The framework consists of a convolutional neural network
(CNN) followed by a recurrent neural network (RNN). The research paper makes use of the
functionalities of Deep Learning and NLP (Natural Language Processing). Image Caption
Generation is an important task as it allows us automate the task of generating captions for any
image. This functionality enables us to easily organize files without paying heed to the task of
captioning. It also presents the implementation of LSTM Method with additional features for a
good performance. Gated Recurrent Unit Method and LSTM Method are evaluated in this
paper. According to the evaluation using BLEU Metrics LSTM is identified as the best method
with 80% efficiency. This approach improves on the best results on the Visual Genome
paragraph captioning dataset. Compared to existing approaches the proposed one shown good
accuracy and efficient by means of time and quality.