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.

NFC Alternative: Transferring Data Between Mobile Devices Using Ultrasound

By Ivan Ozhiganov on October 9, 2013
Data Transfer Between Mobile Devices via Ultrasound Azoft is working on an innovative short-range wireless technology that could serve as an excellent alternative to Near Field Communication (NFC). The idea is to transfer data from one smartphone to another using ultrasound. This technology looks very promising and could be applied in many ways, especially in the area of mobile payments. Our ultrasound technology has a big advantage over NFC: it does not require any additional hardware (i.e. NFC chips) and could be compatible with virtually all smartphones on the market today.

Applied Use of M2M-Technology: Road Sign Recognition in iOS Apps

By Ivan Ozhiganov on July 9, 2013


While machine-to-machine (M2M) technology isn’t a new concept, the area of in-car apps is growing rapidly and providing new and exciting business opportunities, particularly for mobile developers.

Azoft’s first M2M project focuses on integration of mobile apps with car in-dash system. Our team is currently working on an iOS app prototype for recognizing road signs. It's considered to be the first step to a so-called smart dashcam, driver's reliable partner on a road.

Optical Recognition of Credit Card Numbers in an iOS Application

By Ozhiganov Ivan on May 30, 2013
Our recent research on credit card number recognition using an iPhone camera consists of four stages: card image capturing, text area detection, splitting the detected text into four blocks with digits, and finally the digits recognition.

We use Sobel operator and Hough Line Transform method during the image capturing stage and the method of strokes in the splitting stage. The obtained 16 areas with digits are transferred to a multilayer convolutional neural network which contains 3,500 images in the learning base. Even such a small learning base allows our prototype app to provide quite accurate results when recognizing credit card numbers: just 2 errors per 100 test samples.

Algorithm for Identifying Barely Legible or Embossed Text in an Image

By Ivan Ozhiganov on April 11, 2013
The challenge of detecting embossed text, like that on credit cards, has been occupying minds of our R&D team for quite a while. Even though all traditional approaches show little or zero results, Azoft specialists are making small steps closer to the solution. The most recent step made is a modification of stroke width algorithm the implementation of which showed quite well, not yet excellent, results in identifying embossed symbols.

Grayscale Image Sobel Operator Method Otsu Method Strokes

Developing Barcode Scanner Mobile Application for iOS

By Ozhiganov Ivan on February 18, 2013
Our R&D team recently developed a framework for a barcode-scanning application for iOS devices. The requirements stated that this application should be able to scan blurry barcodes that are aligned in any direction without sacrificing the app’s performance. In other words, the user shouldn’t have to align the barcode along the horizontal axis.

Such application requires lots of image processing and therefore relies on a large volume of mathematical calculations. Therefore, we decided to make use of the GPU and divide the process into two main stages. The first stage is to process the image captured by the mobile camera and determine which area is the barcode. The second stage is to read and decoding the barcode.

Hydrodynamic Process Simulation on a Mobile Device

By Ozhiganov Ivan on February 17, 2013
Our R&D team developed a framework for imitating paint dispersion on the surface of water on an iPad. The project involved two main challenges. First, was to come up with an algorithm that provides the most realistic imitation of paint dispersion. The second challenge was to perform calculations on an iPad, taking into consideration its limitations compared to a PC.

Initially, we relied on the Lattice Boltzmann methods in order to simulate fluid dynamics. The experience we gained while working with Lattice Boltzmann methods helped us come up with another algorithm based on Navier-Stokes equations and Kubelka-Munk compositing model. We applied the resulting algorithm for the development of AquaReal app that imitates watercolor painting on an iPad, as well as WaterHockey game. We are currently working on another application that imitates Turkish art of aqueous surface design called Ebru.

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