Optimizer

A seven-segment digits OCR

Optimizer is a seven-segment digits OCR class project carried out by Alex, Priscille, Charlotte and Sacha.

Objective

Approaches

We tried 2 different approaches:

1. The "digit-per-digit" approach

   1. Image processing: identify the screen, crop the picture, grayscale, thresholding, localize digits and crop them.
   2. Learning phase: learn a "MNIST" model that predicts each digit individually.
   3. Inference phase: pass each cropped digit to the "MNIST" model, and append the results.

2. The "end-to-end" approach

   1. Image processing: identify the screen, crop the picture, grayscale and thresholding.
   2. Learning phase: learn a model that predicts all digits at once. We based our model on the "Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks" paper by Goodfellow & al. The idea is to build a ConvNet that simultaneously learns (i) the digits and (ii) where to look for them.

Results

How to reproduce our work and run our models

TBC...

#1 Preprocessing python frame_extractor.py

#2 Preprocessing python digits_cut.py

#3 Model python main.py