font detection

GitHub - robinreni96/Font_Recognition-DeepFont: Its a implementation of DeepFont : Identify Your Font from An Image using Keras

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 .

https://github.com/robinreni96/Font_Recognition-DeepFont

OpenCV Text Detection (EAST text detector) - PyImageSearch

Last updated on July 7, 2021. In this tutorial you will learn how to use OpenCV to detect text in natural scene images using the EAST text detector. OpenCV's EAST text detector is a deep learning model, based on a novel architecture and training pattern.

https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/

How we built a font recognition engine

Yes, we at pixolution are naturally more into images than fonts. But we had an exciting project with Portland based software vendor Extensis who specializes in font asset management and brand management. In this particular case Extensis asked us to train an AI model capable of classifying fonts.

https://pixolution.org/blog/tech/how-we-built-a-font-recognition-engine

Top 10 Deep Learning Algorithms You Should Know in 2021

Deep learning has gained massive popularity in scientific computing, and its algorithms are widely used by industries that solve complex problems. All deep learning algorithms use different types of neural networks to perform specific tasks. This article examines essential artificial neural networks and how deep learning algorithms work to mimic the human brain.

https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm

Machine Learning for Medical Imaging

After completing this journal-based SA-CME activity, participants will be able to: ■ List the basic types of machine learning algorithms and examples of each type. ■ Discuss the typical problems encountered with machine learning approaches. ■ Compute image features and choose methods to select the best features.

https://pubs.rsna.org/doi/full/10.1148/rg.2017160130

Top 10 Deep Learning Algorithms You Should Know in 2021

Deep learning has gained massive popularity in scientific computing, and its algorithms are widely used by industries that solve complex problems. All deep learning algorithms use different types of neural networks to perform specific tasks. This article examines essential artificial neural networks and how deep learning algorithms work to mimic the human brain.

https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm

Complementing Machine Learning Algorithms with Image Processing

Pics, or it did not happen. Taking photos of everyday moments has become today's default. Last year, Keypoint Intelligence projected that humanity would generate 1,436,300,000,000 images. Mylio, an image organization solutions provider, even forecasted this number to hit 1.6 Trillion in 2022. Wow. That's a lot of photos!

https://towardsdatascience.com/complementing-machine-learning-algorithms-with-image-processing-938b1b926014

What is the difference between MLP and RBF?

Thanks for contributing an answer to Cross Validated! Please be sure to answer the question. Provide details and share your research! Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. Use MathJax to format equations. MathJax reference.

https://stats.stackexchange.com/questions/228595/what-is-the-difference-between-mlp-and-rbf

• datasets :
  1. b nazanin

  2. vazir

  3. iran sans

• Enlarge your Dataset

• Choose the algorithm :

  Convolutional Neural Networks

  K Nearest Neighbor

  ✔️ Multi-layer Perceptron (MLP)

  Radial Basis Function Networks (RBFNs)

• Structure :

[ raw dataset ] —> [ Enlarged dataset ]

train

(per font) 1000 [images] →

test

(per font) 200 [images] →

• front end logic :
  • crop the screen shot

  • Normalizing User Input

  • Sending image to server

• utils

  Machine Learning with Python: Neural Networks with Scikit

  In the previous chapters of our tutorial, we manually created Neural Networks. This was necessary to get a deep understanding of how Neural networks can be implemented. This understanding is very useful to use the classifiers provided by the sklearn module of Python.

  https://www.python-course.eu/neural_networks_with_scikit.php

  

  1.17. Neural network models (supervised) - scikit-learn 0.24.2 documentation

  Warning This implementation is not intended for large-scale applications. In particular, scikit-learn offers no GPU support. For much faster, GPU-based implementations, as well as frameworks offering much more flexibility to build deep learning architectures, see Related Projects .

  https://scikit-learn.org/stable/modules/neural_networks_supervised.html

  

  https://www.tandfonline.com/doi/abs/10.1080/014311698214398?journalCode=tres20