In this post, I will show you how to implement real-time text detection and recognition using FOTS: Fast Oriented Text Spotting with a Unified Network.

Here I assume that you are familiar with neural networks, functional flow, custom layers, and training it using tape gradient.

Now the idea is to implement FOTS unified network for text detection. For this, we need 2 models, 1 for detection of the text area (bounding box) and another for the recognition of the text, And an intermediate layer between the 2 models to straight up the angled bounding boxes.

Here we are going to…


A Kaggle Problem.

In a moment of instantaneous motivation, impulsive decision making, and binge-watching movies on AI, I have decided to solve this Kaggle problem. Let’s dive in.

Problem:

In a nutshell, User mobile usage data is provided using which we have to predict the age and gender of the user. Please check the Kaggle site for the complete problem statement.

So, the Final Prediction should be the group which comprises of gender and age range.

eg: M32–38,F24–26 etc.

Data:

Let's focus on “mobile usage data” here.

Akhil Dasari

Aspiring Data Scientist

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