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Integrating the Best Deep Learning Model with Application

In this project we will integrate the Deep Learning model with the application so that its ready for the end user, where anyone can upload the image and get the results, which are English and Hindi text. In this project, we’ll learn to set up the node.js application, integrate the machine learning model using FlaskAPI and make inferences on the images.  
Course
Curriculum

What will you Learn in the Project?

  1. How to install anaconda and requirements (tools) on the local system 
  2. How to create a new anaconda environment and install dependencies
  3. How to run node.js and Flask server 
  4. How to make inference on the application for OCR. 
  5. Creating env variable and installing requirements 
  6. Integrating deep learning models into the application
  7. Making inference on unseen images using the end user application

Tools & Technologies Used

  1. HuggingFace (transformer)
  2. Flask
  3. Node.js
  4. Pillow 
  5. Fairseq

Prerequisite

  1. Understanding of Encoder-Decoder Transformer architecture 
  2. Understanding of TrOCR (for OCR) and IndicTrans (for translation)  models 
  3. Anaconda and requirements installed on the local system 
  4. Basic understanding of Flask API for machine learning

Tasks Performed

Task-1: Setup IndicTrans model and dependencies

Task-2: Integrate Flask API (model) in the Node.js application

Task-3: Testing application with an unseen image (on which model hasn’t been trained)

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Skills you will develop

Run node.js and Flask server

Integrating deep learning models into the application

Making inference on unseen images using the end user application

Creating env variable and installing requirements

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