1 Introduction  
2 Morphological Tools Overview and Applications in NLP  
3 Rule based methods  
3.1 Two-Level Morphology and FST Based Approach  
3.2 Paradigm based approach  
3.3 Stemmer based approach  
4 Machine learning based systems  
4.1 Unsupervised Models  
4.2 Supervised Models  
5 Deep learning based approaches  
5.1 Various Architectures based on Deep Learning  
5.2 Deep Learning based models for Morphological Segmentation  
5.3 Deep Learning based models for Morphological Tagging  
5.4 Joint learning of lemma and morphological tag  
5.5 Transformer based approaches  
6 Datasets for Morphological processing  
7 Discussion: Traditional vs Deep Learning Approaches  
8 Research Issues  
9 Conclusion