How to identify similar words as input from a dictionary
So, your question concerns how to effectively translate the input words into their proposed correct words (e.g. Paruuuu --> Paru) via phonetics and spelling mistake corrections. 

My first idea on this would be to use a deep sequence to sequence model. In a sequence to sequence model, we encode the input word (e.g. Paruuuu) as a sequence of characters into an encoder (effectively an RNN / LSTM, etc.) into a "hidden representation". 
Then you decode your hidden representation as a sequence of phonemes (which denote how we pronounce the word) with a decoder (again, another RNN / LSTM, etc.). 
Then, we can take this sequence of phonemes as input into another encoder and then decode with a neural network, where the output layer is a softmax layer, which computes the probability distribution over all words in your vocabulary (in you Dict terms) and select the word the highest probability.
 So overall, we have:

Encode input word --> Decode output phoneme sequence --> Encode output phoneme sequence --> Decode with neural network to classify word.

 The proposed method is supervised, so of course, you will need examples of input words and their correct "translations" (e.g. ("Paruuu", "Paru")) 

 Here is an article which gives a good intuition behind sequence to sequence models which can classify your input words: https://towardsdatascience.com/understanding-encoder-decoder-sequence-to-sequence-model-679e04af4346