korrekturgrejor

helper/text/overview/faq_discussion/faq.md

@@ -7,7 +7,7 @@
 **A**: This is due to hardware constraints and rate limits imposed by Hugging Face. For alternative ways to use the app, refer to the tab > **Documentation** under > **Duplication for Own Use & API**.
 
 **Q**: <u>Why is Fast track so slow?</u>
-**A**: The current speed is due to hardware limitations and the present state of the code. However, we plan to update the application in future releases, which till significantly improve the performance of the application.
+**A**: The current speed is due to hardware limitations and the present state of the code. However, we plan to update the application in future releases, which will significantly improve the performance of the application.
 
 **Q**: <u>Is it possible to run Fast track or the API on image batches?</u>
 **A**: Not currently, but we plan to implement this feature in the future.

helper/text/overview/htrflow/htrflow_col1.md

@@ -4,7 +4,7 @@ The Swedish National Archives introduces a demonstrational end-to-end HTR (Handw
 
 ## Usage
 
-It needs to be emphasized that this application is intended mainly for demo-purposes. It’s aim is to showcase our pipeline for transcribing historical, running-text documents, not to put the pipeline into large-scale production.
+It needs to be emphasized that this application is intended mainly for demo-purposes. Its aim is to showcase our pipeline for transcribing historical, running-text documents, not to put the pipeline into large-scale production.
 **Note**: In the future we’ll optimize the code to suit a production scenario with multi-GPU, batch-inference, but this is still a work in progress. <br>
 
 For an insight into the upcoming features we are working on:

helper/text/overview/htrflow/htrflow_col2.md

@@ -4,7 +4,7 @@ Please fork and leave a star on Github if you like it! The code for this project
 
 - [Github](https://github.com/Riksarkivet/HTRFLOW)
 
-**Note**: We will in the future package all of the code for mass htr (batch inference on multi-GPU setup), but the code is still work in progress.
+**Note**: We will in the future package all of the code for mass HTR (batch inference on multi-GPU setup), but the code is still work in progress.
 
 ## Models
 

helper/text/overview/htrflow/htrflow_tab1.md

@@ -1,6 +1,6 @@
 ### Binarization
 
-The reason for binarizing the images before processing them is that we want the models to generalize as well as possible. By training on only binarized images and by binarizing images before running them through the pipeline, we take the target domain closer to the training domain, and ruduce negative effects of background variation, background noise etc., on the final results. The pipeline implements a simple adaptive thresholding algorithm for binarization.
+The reason for binarizing the images before processing them is that we want the models to generalize as well as possible. By training on only binarized images and by binarizing images before running them through the pipeline, we take the target domain closer to the training domain, and reduce negative effects of background variation, background noise etc., on the final results. The pipeline implements a simple adaptive thresholding algorithm for binarization.
 
 <figure>
 <img src="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/app_project_bin.png?raw=true" alt="HTR_tool" style="width:70%; display: block; margin-left: auto; margin-right:auto;" >