Font Recognition Using Deep Learning - DeepFont ( Adobe )

DeepFont Paper is a technique created by Adobe.Inc to detect font from images using deep learning . They published their work as a paper for the public . Inspiring their work , I converted their thesis to a working code .

Keypoints of DeepFont:

• Its trained on AdobeVFR Dataset which contains 2383 Font Categories
• Its Domain adapted CNN
• Its Learning is based upon Model Compression

The work is splited into 4 steps :

• Dataset: Since AdobeVFR Dataset datalink is huge in size and contains lot of font categories . We created custom dataset based upon required font patches using TextRecognitionDataGenerator github. The sample folder will be available in this repo.

• Preprocessing of Dataset: Fonts are not like objects , to have to huge spatial information to classify their features . To identify very minute feature change deepfont used certain preprocessing techniques they are

  • Noise
  • Blur
  • Perpective Rotation
  • Shading (Gradient Illumination )
  • Variable Character Spacing
  • Variable Aspect Ratio

• CNN Architecture: Unlike other image classification CNN network , they followed a new schema like two subnetworks,

  • Low Level Sub-Network : Learned from the composite set of synthetic and real-world data.
  • High Level Sub-Network : Learns a deep classifier from the low level features For more details and clarification have a read of their paper

• Framework ( Keras ): As its prototyping , I used Keras to build the entire pipeline . Feel free to prototype in other frameworks.

               Thanks to DeepFont Team for their amazing work
  

Copyright © 2021 Robin Reni. All rights reserved