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Top 5 Open Source AI Solutions for Image Processing

George Miller



Top 5 Open Source AI Solutions for Image Processing
Reading Time: 4 minutes


A few decades or even years ago, self-driving cars and computers with a human-like vision were just a figment of the fantasy writer’s imagination. But today, Artificial intelligence (AI) technologies allow cars to drive safely across busy streets and computers to interpret pictures almost like humans do.

Developers have made impressive progress on how to implement AI on image processing tasks. Furthermore, image processing solutions with AI are widely used in various fields, from medicine to law enforcement to cybersecurity and retail.

AI and machine learning make it possible for the machines to inherit new functionalities through the process of learning. Similarly to small kids, machines can gain the ability to acquire and understand visual information by processing massive amounts of images. To process so much data faster without compromising the final result, developers use image processing models based on machine learning and deep learning algorithms.

In particular, image processing and computer vision solutions use deep learning for accomplishing such complex tasks as:

  • Image classification
  • Object recognition
  • Object tracking
  • Image generation
  • Image retrieval

To implement some computer vision functionalities into your product, you’ll need a large set of tools and resources: image datasets, libraries with code samples, frameworks for creating and training deep learning models. Fortunately, there are a lot of open-course services that you can use to make machine learning less of a mystery and ease the development of your own AI-based image processing solution.

Below, we take a closer look at the five best open source image processing solutions that you can apply to your projects:

  • OpenCV
  • TensorFlow
  • Keras
  • Caffe
  • Google Colab

Let’s get started!


Open Source Computer Vision Library (OpenCV) is one of the most popular open source services for image processing. It’s a free computer vision library that you can use to perform various image processing tasks:

  • Image acquisition
  • Image compression and decompression
  • Image enhancement and restoration
  • Image denoising
  • Image segmentation
  • Data extraction, and more

The library includes numerous algorithms and functions as well as special modules aimed at image processing tasks. Starting from the library version 3.1, there’s also a deep learning module for building and training deep learning models.

With this module, you can use OpenCV to extract features from processed images, and then apply a machine learning model using one of the supported machine learning frameworks.

As of today, OpenCV supports a number of popular frameworks, including TensorFlow, PyTorch, and Caffe. The list of supported types of neural networks includes convolutional, deconvolutional, and recurrent neural networks.

The library is written in C++ and supports C++, Java, Python, and MATLAB interfaces.


TensorFlow is an open-source machine learning framework created by Google. Initially, the project was started for research purposes of the Google Brain team. Today, however, TensorFlow is widely used by both small startups and large companies such as Dropbox, Intel, and Twitter.

You can use TensorFlow to process different types of data, but in relation to image processing, this framework works best for:

  • Image classification
  • Image recognition
  • Image segmentation
  • Image to image translation (pix2pix)

TensorFlow includes a set of libraries for creating and training custom deep learning models and neural networks. The framework supports Jupyter notebooks and provides a style guide with recommendations on writing readable, consistent code.

TensorFlow supports several popular programming languages, including C++, Python, Java, Rust, and Go. However, you can install third-party bindings for other languages, such as Ruby, Scala, or PHP.


Keras is an open-source Python library for creating deep learning models. It’s a great solution for those who only begin to use machine learning algorithms in their projects as it simplifies the creation of a deep learning model from scratch. Keras is easy to manage and it is suitable for fast and simple prototyping of different types of neural networks.

The library was built on TensorFlow and is currently fully integrated into the framework. This means that you can write your deep learning model in Keras, as it has a much more user-friendly interface, and then easily implement a specific functionality or feature from TensorFlow in this model.

Keras can also be deployed on top of other popular AI frameworks such as Microsoft Cognitive Toolkit and Theano.


Convolutional Architecture for Fast Feature Embedding (Caffe) is an open-source framework that can also be used for creating and training popular types of deep learning architectures. You can use Caffe to accomplish such tasks as image classification, segmentation, and recognition.

Caffe is written in C++ but it also has a Python interface. The framework supports both CPU- and GPU-based accelerated libraries such as NVIDIA cuDNN and Intel MKL. The framework also has a special database, Caffe Model Zoo, containing a set of pre-trained deep learning models. As of today, it offers four BAIR-trained models:

  • BAIR Reference CaffeNet
  • BAIR Reference R-CNN ILSVRC-2013
  • BAIR AlexNet
  • BAIR GoogLeNet

Model Zoo also includes a number of community models trained by other Caffe users. As for the types of supported neural networks, Caffe works best with convolutional neural networks (CNN) and feedforward networks. At the same time, it’s not the best choice for training recurrent neural networks.

In 2017, Facebook launched Caffe2, an open-source framework for training and deploying deep learning models. And in 2018, Caffe2 was integrated with another popular AI framework, PyTorch.

Caffe2 comes with C++ and Python APIs and supports all popular platforms.

Google Colaboratory (Colab)

Google Colaboratory, or simply Colab, is one of the top image processing services. While it’s rather a cloud service than a framework, you can still use Colab for building custom deep learning applications from scratch. With the help of Colab, you can perform such image processing tasks as image classification, segmentation, and object detection.

Google Colab eases the use of other popular AI-based tools such as OpenCV, TensorFlow, and Keras. The service uses Jupyter Notebooks, helping developers to share their knowledge, tips, and best practices on building AI-based applications. Plus, in contrast to other similar services, Colab offers free usage of both CPU- and GPU-based acceleration.


Machine learning models and algorithms help developers implement specific image processing functionalities into their products both quickly and easily. However, building a custom machine learning model or neural network requires lots of resources and a high level of technology expertise. With the help of the listed open-source tools, libraries, and frameworks, you can simplify the process of leveraging Artificial Intelligence technologies to your benefit.


This article is a contribution from Marcell Gogan.  Marcell is a specialist within digital security solutions, business design and development, virtualization and cloud computing, R&D projects, establishment and management of software research direction – working with Ekran System. He also loves writing about data management and cybersecurity. 

George Miller started his career in content marketing and has started working as an Editor/Content Manager for our company in 2016. George has acquired many experiences when it comes to interviews and newsworthy content becoming Head of Content in 2017. He is responsible for the news being shared on multiple websites that are part of the European Gaming Media Network.

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Industry News

Codere Appoints Luis Sánchez Serrano as Corporate Director of Technology and Digitalisation

Niji Narayan



Codere Appoints Luis Sánchez Serrano as Corporate Director of Technology and Digitalisation
Photo Source:
Reading Time: 1 minute


Gambling group Codere has appointed Luis Sánchez Serrano as the Corporate Director of Technology and Digitalisation. Luis Sánchez will report directly to the Group’s general director, Vicente Di Loreto.

Luis Sánchez has more than 23 years of experience, mainly in the banking industry, in companies such as Liberbank, Bankinter, Banco Espirito Santo, has proven capabilities in the design and implementation of transformation projects, innovation and digitalisation of organisations , as well as a broad strategic and transversal vision of the main processes and technological solutions of the market.

“This incorporation strengthens our team and consolidates the strategy of change and transformation undertaken in our group to continue fulfilling our commitment to the client and creating value,” Vicente Di Loreto said.

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Industry News

Pollard Banknote Adds GALAGA™ to its Portfolio of Licensed Brands

Niji Narayan



Pollard Banknote Adds GALAGA™ to its Portfolio of Licensed Brands
Reading Time: 1 minute


Pollard Banknote Limited has added the classic arcade game GALAGA™ to its portfolio of licensed brands. Under an agreement with BANDAI NAMCO Entertainment America Inc., Pollard Banknote will offer GALAGA™ branded instant tickets to its lottery clients. Pollard Banknote’s suite of licensed brands includes several powerhouse arcade games from the early 1980s.

As per the agreement, Pollard Banknote will leverage the GALAGA™ signature logo and colourful pixilated icons for eye-catching ticket designs and promotional merchandise. The ticket artwork will utilise distinctive GALAGA™ imagery to mimic the game’s acclaimed playstyle. GALAGA™ branded merchandise, including full-sized vintage arcade games, will be available to lotteries for social media contests and second chance draws.

“Pollard Banknote’s retro arcade brands have captivated lottery players around the globe, and we are confident that GALAGA™ will give players more of the classic arcade look and feel they love. We look forward to helping lotteries bring GALAGA™ to market, supported by our 360˚ approach to marketing and promotional programs that promote player excitement and engagement,” Brad Thompson, VP of Sales and Marketing at Pollard Banknote said.

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Industry News

Evolution Gaming: Interim report January-June 2019

George Miller



Evolution Gaming: Interim report January-June 2019
Reading Time: 3 minutes


Evolution released today its Interim Report for January-June 2019.


Second quarter of 2019 (Q2 2018)

  • Operating revenues increased by 45% to EUR 85.7 million (59.3)
  • EBITDA increased by 63% to EUR 42.7 million (26.2), corresponding to a margin of 49.8% (44.2)
  • Profit for the period amounted to EUR 34.5 million (20.1)
  • Earnings per share amounted to EUR 0.19 (0.11)

January-June 2019 (1H 2018)

  • Operating revenues increased by 49% to EUR 165.0 million (110.8)
  • EBITDA increased by 63% to EUR 78.6 million (48.1), corresponding to a margin of 47.7% (43.4)
  • Profit for the period amounted to EUR 63.1 million (36.7)
  • Earnings per share amounted to EUR 0.35 (0.20)

Comments from CEO Martin Carlesund:

“Evolution’s strong development continues, and we can report continued high growth and further improved margin in the second quarter. 2019 is a year of product and innovation, and during the period, we have launched all this year’s new games. The response has been instant, and we are overwhelmed by the positive reception among both players and operators. Our aim in the development has been to create games that attract new player types and to expand the Live vertical into new segments, and so far, we are very happy with the outcome. All games, from the new titles within the Game Show category to the dice games, find their audiences and we see a significant increase in the number of players. The new games are not as staff intensive as the traditional table games, which in combination with a generally high efficiency in all studios contribute to the margin development. The extended portfolio also contributes to the revenue growth, but it is important to note that most of our revenues continue to derive from our core games and that the new games are still in an early stage post-launch. However, all new titles contribute to a generally higher interest in Live Casino and we believe that the Live vertical will continue to grow its share of the total online casino market. When the Live vertical grows, we take market shares – as do our customers with access to the new games.

Revenues amounted to EUR 85.7 million, which is 45 percent higher than the corresponding quarter last year. EBITDA amounted to EUR 42.7 million with a margin of 49.8 percent, which is the highest margin recorded so far in a single quarter. In the first half of the year, the margin was 47.7 percent, and we expect to be able to sustain this level in the second half as well which is why we increase our expected margin range to 47-49 percent for the 2019 full year.

The positive market development continues. The Nordics are growing, however at a somewhat slower pace as the Swedish market now is normalising following the intense start to the year as a result of the new gaming legislation. The UK continues to stabilise and is growing compared to the corresponding quarter last year. Both Rest of Europe and Rest of World also exhibit favourable growth.

Going forward, we will continue to invest in the development of both additional game shows and other innovative product types, as well as table games to meet the overall demand for Live. We also continue to invest in our studios. During the quarter, we have transferred the first tables to the new Malta studio, while also expanding the studio in Georgia. In addition, we have initiated a doubling of the capacity in New Jersey. We will also strengthen our presence in the US further and have started the planning for the construction of a studio in Pennsylvania. All in all, investments for the 2019 full year in absolute numbers will be somewhat higher than in 2018. It is imperative for us to meet the demand for Live, to continue to enable innovation within the company and to constantly increase the gap to competition. Connected to this, it is important to remember that in a case where we must prioritise, we will always put growth before margins.

During the first half of the year, we have laid the foundation for Evolution’s continued leadership in Live Casino. A proof of this was when we received the award as Live Casino Supplier of the Year for the tenth consecutive year at the EGR B2B Awards in June. The award goes straight to all our employees. I am immensely proud of everyone’s achievements so far this year, and the innovation power they possess creates confidence for the future.

Since Evolution’s inception, we have had a paranoid approach to our development, and given all achievements so far in 2019 I would like to conclude these comments by pointing out that we are never fully satisfied, we never sit back and each day, we fight to become a little bit better.”


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