Azoft RnDCase Studies

Research and Development

Azoft R&D department handles the most challenging, non-standard issues that arise when implementing software solutions. Many of the problems tackled by our R&D team strongly rely on scientific research and may involve technical risks. If you have an idea or a technologically complex project but aren't sure how to make it work, Azoft R&D team can study the problem and come up with a technologically sound approach. In this section you can see some of the projects we have recently completed.

Information Retrieval from OCR Text

By Ozhiganov Ivan on December 14, 2016

Information retrieval from OCR text

Information retrieval is an essential stage in the analysis of camera-captured text data as there are many areas where it can be used. For example, it is highly demanded in the receipt recognition services because the quality of the recognized texts needs to be improved. To improve the effectiveness of the information retrieval from OCR texts, our R&D engineers have used LSTM recurrent neural network.

Object Detection Using Fully Convolutional Neural Networks

By Ivan Ozhiganov on November 24, 2016

Object detection Using Fully Convolutional Networks

Research on artificial neural network training is most widely used to automate multimedia data recognition. Among other things, it’s important that various types of neural networks show strong results in the tasks of speech and image recognition, financial forecasts, machine translation, and many others. We have effectively applied fully convolutional neural networks within the receipt recognition research.

Applying OCR Technology for Receipt Recognition

By Ozhiganov Ivan on April 7, 2016

Applying OCR Technology for Receipt Recognition

When it comes to the receipt recognition, it often relates to the appearance of complications with OCR methods. Extracting information from receipts implies taking a raw, badly structured text with custom characters and displaying normalized data on a device screen. Convolutional neural networks (CNN) can facilitate this process. Using CNN, we work on the comprehensive solution that can be easily adjusted to process receipts from different countries.

Convolutional Neural Networks for Object Detection

By Ivan Ozhiganov on February 25, 2016

Convolutional Neural Networks for Object Detection

Deep learning is an extremely popular field these days. Convolutional Neural Networks have successfully proven themself in computer vision. As a result, we decided to test in practice the effectiveness of convolutional neural nets for object detection in images. As the object of our research, we chose license plate and road sign pictures.

Classification of EEG Signals for Brain-Computer Interface

By Ozhiganov Ivan on October 20, 2015

Classification of EEG Signals for Brain-Computer Interface

Using brain-computer interface could facilitate everyday life of paralyzed or disabled patients. With a spate of interest in this topic, Azoft R&D department, together with Sergey Alyamkin and Expasoft participated in "Grasp-and-Lift EEG Detection" competition organized by Kaggle. The competition was dedicated to classifying various movements of the right hand via EEG (electroencephalogram) for a brain-computer interface development.

Machine Learning Methods in Video Annotation

By Ivan Ozhiganov on July 10, 2015

Machine Learning Methods in Video Annotation

Research and development in the field of image and video processing is an important and popular task today. In the era of mobile technologies almost anyone can make a video and upload it on youtube or any other hosting service. To find relevant videos, they need to be classified. One way to handle video classification is by using tags annotation.

Developing a Face Recognition System Using Convolutional Neural Network

By Ivan Ozhiganov on May 14, 2015

Developing Face Recognition System Using Convolutional Neural Network

The last decade has become the breakthrough in artificial neural networks development : this technology now is widely used in the areas, where traditional algorithms could not cope with the tasks. Spam filtering, contextual advertising, gambling, recognition of texts and characters, and more — neural networks show great results.

Security is one of the most important applications of neural networks. Networks are used for surveillance and monitor suspicious banking transactions. Azoft R&D department is currently engaged in the development and training of such a network: we create a face recognition system that is invariant to face positions in the space, turns, lighting and facial expressions.

Real-Time Video Processing Algorithm for Instant License Plate Recognition in iOS Apps (using OpenCV & GPGPU)

By Ivan Ozhiganov on May 29, 2014
Real-time Video Processing Algorithm for Instant License Plate Recognition in iOS Apps (using GPGPU & OpenCV)

Since Azoft’s first OCR project we were exploring the GPGPU technology and the opportunities it provides for character recognition. Now our team is working on an iOS app prototype for recognizing license plate that uses GPGPU. The key feature that makes our solution unique is real-time video processing that makes instant number recognition possible.
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