Fix uppercase images

Author: annietllnd

content/learning-paths/embedded-and-microcontrollers/avh_ppocr/end-to-end_workflow.md

@@ -14,7 +14,7 @@ This section provides hands-on instructions for you to deploy pre-trained Paddle
 
 The steps involved in the model deployment are shown in the figure below:
 
-![End-to-end workflow#center](./Figure3.webp "Figure 3. End-to-end workflow")
+![End-to-end workflow#center](./figure3.webp "Figure 3. End-to-end workflow")
 
 ## Deploy PaddleOCR text recognition model on the Corstone-300 FVP included with Arm Virtual Hardware
 
@@ -80,7 +80,7 @@ Training the model usually takes a lot of time. In step 2, an already trained En
 
 By default, the script uses the image shown below (QBHOUSE) as an example to verify the inference results on the Corstone-300 FVP with Arm Cortex-M55.
 
-![QBHOUSE#center](./Figure4.png)
+![QBHOUSE#center](./figure4.png)
 
 Make the script executable with `chmod`.
 

content/learning-paths/embedded-and-microcontrollers/avh_ppocr/overview.md

@@ -16,7 +16,7 @@ OCR has been widely used in many industry scenarios such as ticket information e
 
 Text recognition is a sub-task of OCR. It's the step after text detection in OCR's two-stage algorithm which converts image information into text information. 
 
-![Example of English text recognition #center](./Figure1.png "Figure 1. Example of English text recognition (Image source: https://iapr.org/archives/icdar2015/index.html)")
+![Example of English text recognition #center](./figure1.png "Figure 1. Example of English text recognition (Image source: https://iapr.org/archives/icdar2015/index.html)")
 
 
 In this Learning Path, you will learn how to apply deep learning (DL) to the OCR text recognition task and setup a development flow from model training to application deployment. 
@@ -39,7 +39,7 @@ As seen in Figure 2, the overall pipeline of PP-OCRv3 is similar to PP-OCRv2 wit
 
 For example, the text recognition model introduces [SVTR](https://arxiv.org/abs/2205.00159) (Scene Text Recognition with a Single Visual Model) based on PP-OCRv2. The model also uses [GTC](https://arxiv.org/pdf/2002.01276.pdf) (Guided Training of CTC) to guide training and model distillation. For more details, please refer to this PP-OCRv3 [technical report](https://arxiv.org/abs/2206.03001v2).
 
-![PP-OCRv3 pipeline diagram #center](./Figure2.png "Figure 2. PP-OCRv3 pipeline diagram (Image source: https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/PP-OCRv3_introduction_en.md)")
+![PP-OCRv3 pipeline diagram #center](./figure2.png "Figure 2. PP-OCRv3 pipeline diagram (Image source: https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/PP-OCRv3_introduction_en.md)")
 
 In the next section, you will deploy a trained PP-OCR text recognition model on the Arm Corstone-300 FVP.
 

content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/FVP running.webp

@@ -89,4 +89,4 @@ Ensure your FVP instance is running and matches the model and parameters selecte
 
 After these steps, you can debug the software stack as shown in the following figure:
 
-![FVP running #center](Select_target.webp "Debug interface in GUI")
+![FVP running #center](select_target.webp "Debug interface in GUI")

content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md

@@ -101,4 +101,4 @@ Always check the name of the CPU instance when switching between different FVP m
 
 You can also run the FVP using its graphical user interface:
 
-![GUI #center](FVP.webp "View of the FVP GUI")
+![GUI #center](fvp.webp "View of the FVP GUI")

content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md

@@ -70,7 +70,7 @@ Now run the Mobilenet V2 computer vision model, using [executorch/examples/arm/r
 
 Observe that the FVP loads the model file, compiles the PyTorch model to ExecuTorch `.pte` format and then shows an instruction count in the top right of the GUI:
 
-![Terminal and FVP output#center](./Terminal%20and%20FVP%20Output.jpg "Terminal and FVP output")
+![Terminal and FVP output#center](./terminal_and_fvp_output.webp "Terminal and FVP output")
 
 {{% notice Note %}}
 

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/7-configure-fvp-gui.md

@@ -86,7 +86,7 @@ To do so you need to go to go to `Tools -> WindowsPerf Host Data` scroll to the
 6. **Saving Your Settings**:
    - Once you are satisfied with your configurations, click `save`.
 
-![SPE settings #center](./SPE-settings.webp)
+![SPE settings #center](./spe-settings.webp)
 
 ## Initiating the Sampling Process
 

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.webp renamed to content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/terminal_and_fvp_output.webp

@@ -9,16 +9,16 @@ The render graph in Frame Advisor shows a visualization of the rendering operati
 
 Render passes flow from left to right. The render pass that outputs to the swapchain is the final render pass that outputs to the screen.
 
-![The Render Graph view in Frame Advisor alt-text#center](FA_render_graph_1.1.gif "Figure 1. The Render Graph view")
+![The Render Graph view in Frame Advisor alt-text#center](fa_render_graph_1.1.gif "Figure 1. The Render Graph view")
 
 1. Here, we can see some output attachments that are not used in a future render pass.
 
-    ![Redundant output attachments alt-text#center](Render_graph_egypt_redundant_attachments.png "Figure 3. Redundant output attachments")
+    ![Redundant output attachments alt-text#center](render_graph_egypt_redundant_attachments.png "Figure 3. Redundant output attachments")
 
     You should clear or invalidate input and output attachments that are not used to avoid unnecessary memory accesses. If clear or invalidate calls are present within a render pass, they are shown in the `Frame Hierarchy` view.  
 
 1. In this example, we can see that some render passes have no consumers at all and that they do not contribute to the final rendered output.
 
-    ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](Render_graph_egypt_redundant_rps.webp "Figure 4. Redundant render passes")
+    ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](render_graph_egypt_redundant_rps.webp "Figure 4. Redundant render passes")
 
     These render passes could therefore be removed, without affecting the output, saving processing power and bandwidth.

content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md

@@ -15,9 +15,9 @@ The demo is named **Steel Arms**. Created with Unreal Engine 5.3, Steel Arms bri
 The following screenshots are from scenes in **Steel Arms** which is powered by Unreal Lumen. Several optimization tips and techniques were used in the development of **Steel Arms** for achieving the best performance with Lumen. This learning path will start with an introduction to ray tracing and then cover the best practices for hardware ray tracing in Lumen.
 
 
-![](images/Garage.webp)
+![](images/garage.webp)
 
-![](images/Garage2.webp)
+![](images/garage2.webp)
 
 
 ## What is Ray Tracing?