Last month, Microsoft released .NET Core 3.0 that allows developers to port Windows Forms and Windows Presentation Foundation (WPF) projects to the .NET Core projects. However, the feature is still Windows-only, which may disappoint someone who wants to create cross-platform GUI apps based on .NET Core. In this article, I will share how to create a simple Windows GUI barcode reader app on .NET Framework, and then port it to .NET Core.Read more
WASI is a modular system interface, which aims to build runnable .wasm modules for any WASI-compliant runtime, not only for Node.js and web browsers. Although WASI is still in development and not stable yet, it is available for some experimental projects. In this article, I will share how to use WASI SDK to build a .wasm barcode reader module by porting ZXing C++.Read more
Direct Part Marking (DPM) is a process to mark equipment with some information, such as barcodes, permanently. The typical DPM barcode symbologies include DataMatrix and QR code. Since version 7.2, Dynamsoft Barcode Reader SDK has been capable of decoding DPM barcodes. In this article, I will share how to create a simple python barcode reader to read the DPM DataMatrix code.
When evaluating an image processing and recognition SDK, image dataset is vital for benchmarking the performance. Google is absolutely the best place for finding and downloading required images. An automation tool would be handy for getting amounts of image files rapidly. In this article, I will share how to use Python to download barcode images from Google, as well as how to test Dynamsoft Barcode Reader SDK with the image set.
Dynamsoft Barcode Reader is an enterprise-class barcode SDK implemented in C/C++. Although the SDK is available for Windows, Linux, and macOS, there is only one Windows sample showing how to invoke the latest video APIs in version 7.x. To make developers experience the example in Linux or other platforms, I decided to refactor the project build environment with CMake.
Dynamsoft Barcode Reader 7.0 brings a set of thread-based APIs for continuous frame management and corresponding barcode decoding. It extremely simplifies the programming complexity, especially for Python. It is known that Python’s GIL (Global Interpreter Lock) affects the performance in a multi-threaded scenario. Running computation intensive tasks in Python thread cannot improve the Python app performance. If you create a Python barcode scanner app with OpenCV and Dynamsoft Barcode Reader 6.x, Python multiprocessing is the only way for getting a high camera frame rate. With the thread-based APIs of Dynamsoft Barcode Reader 7.x, your Python apps will not be limited by GIL anymore. This tutorial shares how to integrate the thread-based C/C++ APIs into Python barcode extension.
If you want to quickly create a walking robot, you can use the Lego Boost. In this post, I will share how to use a webcam, Lego Boost, and Dynamsoft Barcode Reader SDK to make a robot for scanning barcodes. The programming language used in this article is Python.
Assume you apply barcode technology to the logistics conveyor belt for scanning parcels. A problem you may face is how to recognize barcodes from blurred images. Although we can use advanced algorithms to deal with this complicated case, we’d better improve the image quality as possible as we can. A simple way is to adjust the camera shutter speed which is also known as exposure time. Faster shutter speed can avoid motion blur. In this post, I will share how to invoke Android Camera2 APIs to change the shutter speed, as well as how to build a simple Android barcode reader to decode barcodes from fast-moving objects
If you want to use Raspberry Pi as an economical way of detecting barcodes, you can take account for Dynamsoft Barcode Reader SDK. As a business software, Dynamsoft Barcode Reader SDK is designed for overcoming a variety of complicated scenarios with sophisticated algorithms and heavy computations. Although the SDK is flexible for customizing algorithm parameters, subject to the low-frequency CPU of Raspberry Pi, the tradeoff between recognition accuracy and detection speed is still a big challenge. In this article, I will use Socket client-server model as a substitute solution. Thanks to Sabjorn’s NumpySocket module.