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Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community.In daily life text is present in variety of natural scenes such as road signs, shop names, posters, signboards etc. Sometime these text present in images even help one to understand context of Image.In this work we are going to implement the research paper FOTS which focus on detection and recognition of text at the same time from the real world images.

Contents

  1. Business Problem
  2. ML formulation
  3. Source of Data
  4. EDA
  5. Data Generation
  6. Overall architecture of FOTS
  7. Losses used
  8. Model Training
  9. Inference Pipeline


Instead of waking to overlooked “Do not disturb” signs, Airbnb travelers find themselves rising with the birds in a whimsical treehouse, having their morning coffee on the deck of a houseboat, or cooking a shared regional breakfast with their hosts

New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.

Contents

  1. Business Problem
  2. Use of ML
  3. Source of Data
  4. Existing Approaches
  5. My…

sugam verma

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