Native Library (C/C++, STL, Neural Networks)
The cross platform library was designed for image processing and recognition across desktop and mobile platforms. OpenCV was chosen for the input frame pre-processing, contour analysis, and recognition area setting. Image recognition was implemented using a neural network-based classifier.
Desktop Client/Trainer (C++, Qt 5.x, QML)
Our team developed a desktop application using Qt 5.x framework and Qt QML technology. This application was used for neural network training and control as well as for testing. Webcam was used instead of the mobile phone for data input.
Android Client (Java, Android SDK, Android NDK, RealmDB, rxJava, Retrofit2)
Our developers implemented the mobile application with Native library features for image recognition, domain logic for data analysis, and auxiliary functional units. To ensure offline operation of the application, the local RealDB database, a high-performance noSQL database for mobile platforms, was implemented.
Web API Server (Java, Spring Framework, MongoDB)
Centralized data storage and mobile client request processing were implemented in a Web Server, based on Java and Spring Framework. Our software engineers settled on MongoDB as a server database due to its high performance and speed parameters.
The process of Digital Transformation really forced our developers to push their limits and come up with innovative solutions tailored to the client’s needs. The outstanding results were achieved through creative cooperation and mutual support.