Fixed documentation for concurrent use of DL Streamer and DeepStream

docs/source/dev_guide/concurrent.md

@@ -1,12 +1,17 @@
 # Concurrent Use of DL Streamer and DeepStream
 
 This tutorial explains how to simultaneously run DL Streamer and DeepStream on a single machine for optimal performance.
+The purpose of this sample is to demonstrate how additional machine resources can be utilized. For example, if a user runs detection on an NVIDIA GPU, they can simultaneously execute a DL Streamer pipeline on an Intel integrated GPU, NPU, or CPU. This approach enables more efficient utilization of the system's available compute resources.
 
 ## Overview
 
 Systems equipped with both NVIDIA GPUs and Intel hardware (GPU/NPU/CPU) can achieve enhanced performance by distributing workloads across available accelerators. Rather than relying solely on DeepStream for pipeline execution, you can offload additional processing tasks to Intel accelerators, maximizing system resource utilization.
 
-A Python script (`concurrent_dls_and_ds.py`) is provided to facilitate this concurrent setup. It assumes that Docker and Python are properly installed and configured. The Ubuntu 24.04 is currently the only supported operating system.
+A Python script [concurrent_dls_and_ds.py](https://github.com/open-edge-platform/dlstreamer/blob/main/samples/gstreamer/python/concurrent/concurrent_dls_and_ds.py) is provided to facilitate this concurrent setup. It assumes that Docker and Python are properly installed and configured. The Ubuntu 24.04 is currently the only supported operating system.
+
+## Detection algorithm
+
+The DL Streamer pipeline performs license plate detection and subsequently applies OCR to recognize the text. In contrast, the DeepStream pipeline first detects the vehicle, then identifies the license plate within the detected vehicle object, and finally performs OCR to recognize the text.
 
 ## How it works