change the markdown from olof and erik feedback

helper/text/overview/changelog_roadmap/changelog.md

@@ -1,6 +1,8 @@
 ## Changelog
 
-### [0.0.1] - 2023-10-19
+All notable changes to HTRFLOW will be documented here.
+
+### [0.0.1] - 2023-10-23
 
 #### Added
 
@@ -8,14 +10,13 @@
 
 #### Fixed
 
-- Fixed naming conventions of tabs in app
+- Fixed naming conventions of tabs in app so they are more coherent with the code.
 
 #### Changed
 
 - Changed the layout in both Fast track and Stepwise to improve the UX
+
   - Examples are viewed in the middle of the layout
   - "Advanced settings" are initial hidden
 
-#### Removed
-
 - Removed **help** tab for now (documentation of Fast track and Stepwise will come in a later release)

helper/text/overview/changelog_roadmap/roadmap.md

@@ -1,23 +1,27 @@
 ## Roadmap
 
-- Release Training and Eval data on HuggingFace
+Our roadmap is where you can learn about what features we're working on. Have any questions or comments about items on the roadmap? See **Overview** > **FAQ & Discussion** for feedback or collaboration.
 
-- Specialized TrOCR Model
-- Add support for TrOCR models as Text recognition model
+### Working on
 
-  - Train a TrOCR model specialized on Swedish historical handwritten text.
+- Release Training and Eval data on HuggingFace
 
-- Initial document classifier
+- Add support for TrOCR models as Text recognition model:
 
-- Optimize the backend of the application
+  - Train a TrOCR model specialized on Swedish historical handwritten text.
 
+- Optimize the backend of the application:
   - Package the code
   - Add support for batch inference
   - Start a collaborative open source project
 
-- Add support for Different segmentation strategies
+### Backlog
+
+- Initial document classifier
+
+- Add support for Different segmentation strategies:
 
   - Table detection
   - Different text layouts with multiple columns
 
-- Add more endpoints for the rest api and documentation
+- Add more endpoints for rest api and add a more extensive documentation

helper/text/overview/contributions/contributions.md

@@ -1,27 +1,33 @@
-## Project Contributions
+## HTRFLOW – Contributions
 
-We extend our deepest gratitude to the individuals and organizations who have made this project possible through their invaluable contributions, especially in providing datasets for training the models. Their generosity and collaboration have significantly propelled the project forward.
+The AI models used in HTRFLOW is the result of a collaborative effort, involving the National Archives in both Sweden and Finland, in partnership with the Stockholm City Archives, Jämtlands läns fornskriftsällskap, citizen science volunteers and researchers from Stockholm and Uppsala Universities.
 
-### Datasets Contributors
+Several datasets have been created by participants through Citizen Science using the Handwritten Text Recognition (HTR) software, Transkribus, provided by [READ-COOP SCE](https://readcoop.eu/) .
 
-- [Name/Organization]: Provided [Name of Dataset] which was instrumental in training [Specify Model].
-- [Name/Organization]: Contributed [Name of Dataset] that greatly enhanced the performance of [Specify Model].
-- [Name/Organization]: Generously shared [Name of Dataset] enabling us to refine [Specify Model].
-- ... (continue listing contributors as necessary)
+### Archives used to train models for HTRFLOW
 
-### Other Contributions
+[Svea hovrätt (Renskrivna protokoll), 1713–1735](https://sok.riksarkivet.se/arkiv/H2hpDbNn14scxjzdWqAaJ1)
 
-- [Name/Organization]: For [description of contribution, e.g., code, testing, design].
-- ... (continue listing contributors as necessary)
+[Bergmästaren i Nora m fl bergslag (Hammartingsprotokoll), 1698–1765](https://sok.riksarkivet.se/arkiv/M5Fe2TT9rH6cxG02H087k3)
 
-### Special Mentions
+[Trolldomskommissionen, mainly 1670s](https://sok.riksarkivet.se/trolldomskommissionen)
 
-- ... (mention any other individuals/organizations that played a significant role)
+[Bergskollegium, 1718–1758](https://sok.riksarkivet.se/arkiv/SMFky31ekQ80Qsk0UCZZE2)
 
-We are immensely thankful for the collective effort and dedication that has significantly contributed to the progress of this project. The open collaboration and sharing of resources underscore the community’s commitment to advancing the field.
+[Jämtlands domsaga, 1647–1688](https://sok.riksarkivet.se/arkiv/2l4NYFT8rH6cxG02H087k3)
 
-For further details on contributions or if you are interested in contributing, please refer to our Contribution Guidelines or contact [Your Contact Information].
+[Stockholms domkapitel, 1728–1759](https://sok.riksarkivet.se/arkiv/etg1tyeEaIPMBzKbUKTjw1)
 
-Thank you!
+[Politikollegiet, 1729–1759](https://sok.riksarkivet.se/arkiv/1lQnXIDiKaYxRLBlK1dGF3)
 
-// Riksarkivet
+[Göteborgs poliskammare före 1900 (Detektiva polisens rapportböcker), 1868–1901](https://sok.riksarkivet.se/arkiv/oLTOi9yxweZJUG018W43t3)
+
+[Renovated Court Records, the National Archives of Finland, 1800s](https://tuomiokirjat.kansallisarkisto.fi/)
+
+### Ongoing research collaborations
+
+[Transcription node Sweden – machine interpretation and citizen research combined](https://riksarkivet.se/forskning), Swedish National Archives and University of Gothenburg, funded by the Swedish National Heritage Board.
+
+[Mapping the geographies of early modern mining knowledge. A digital history of the study tours of the Swedish Bureau of Mines, 1691–1826](https://www.idehist.uu.se/forskning/projekt/den-tidigmoderna-bergsvetenskapens-geografier), Uppsala University and Stockholm University, funded by the Swedish Research Council.
+
+The Swedish National Archives' research and development on HTR is part of the Swedish national infrastructure Huminfra. [Click here](https://riksarkivet.se/huminfra) for more information.

helper/text/overview/contributions/huminfra_image.md

@@ -0,0 +1,3 @@
+<a href="https://www.huminfra.se/">
+<img src="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/Huminfra_logo.png?raw=true" width="17%" align="left" margin-right="100" />
+</a>

helper/text/overview/contributions/riksarkivet_image.md

@@ -0,0 +1,3 @@
+<a href="https://riksarkivet.se">
+<img src="https://raw.githubusercontent.com/Borg93/Riksarkivet_docs/main/docs/assets/fav-removebg-preview.png" width="17%" align="right" margin-right="100" />
+</a>

helper/text/overview/duplicate_api/duplicate.md

@@ -2,7 +2,7 @@
 
 Please be aware of certain limitations when using the application:
 
-- Primarily, this application is designed for demonstration purposes and is not intended for mass HTR.
+- This application is primarily designed for demonstration purposes and is not intended for scaling up HTR.
 - Currently, the Swedish National Archives has constraints on sharing hardware, leading to a queue system for high demand.
 - The demo is hosted on Hugging Face domains, and they may rate-limit you if there's an excessive number of requests in a short timeframe, especially when using the API.
 

helper/text/overview/faq_discussion/discussion.md

@@ -1,11 +1,7 @@
-## Discussion about the app
+## Contact us
 
-If you have suggestions, questions, or would like to discuss improvemnts for the app, please don't hesitate to reach out.
+If you have any questions, or suggestions for features or improvements, please don’t hesitate to contact us.
 
 - Open a discussion on [HuggingFace](https://huggingface.co/spaces/Riksarkivet/htr_demo/discussions).
 
-## Contact us
-
-If you prefer email or your question is more sensitive please email.
-
-- Send it to forskning@riksarkivet.se
+- Send an email to: forskning@riksarkivet.se

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 will significantly improve run time and 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 till significantly improve the performance of the application.
 
-**Q**: <u>Is possible to run Fast track or the API on multiple images on same time?</u>
-**A**: Not currently, but we plan to add this feature in the future.
+**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

@@ -1,11 +1,12 @@
 ## Introduction
 
-The Swedish National Archives introduces a demonstrational end-to-end HTR (Handwritten Text Recognition) pipeline. This pipeline comprises two instance segmentation models: one designated for segmenting text-regions and another for isolating text-lines within these regions, coupled with an HTR model for image-to-text transcription. The objective of this project is to establish a generic pipeline capable of processing running-text documents spanning from 1600 to 1900.
+The Swedish National Archives introduces a demonstrational end-to-end HTR (Handwritten Text Recognition) pipeline. The pipeline consists of two instance segmentation models, one trained for segmenting text-regions within running-text document images, and another trained for segmenting text-lines within these regions. The text-lines are then transcribed by a text-recognition model trained on a vast set of swedish handwriting ranging from the 17th to the 19th century.
 
 ## Usage
 
-It's crucial to emphasize that this application serves primarily for demonstration purposes, aimed at showcasing the various models employed in the current workflow for processing documents with running-text. <br>
+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.
+**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:
 
-- Navigate to the > **About** > **Changelog & Roadmap**.
+- Navigate to the > **Overview** > **Changelog & Roadmap**.

helper/text/overview/htrflow/htrflow_col2.md

@@ -2,12 +2,13 @@
 
 Please fork and leave a star on Github if you like it! The code for this project can be found here:
 
-- [Github](https://github.com/Borg93/htr_gradio)
-  **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.
+- [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.
 
 ## Models
 
-The models within this pipeline will be subject to continual retraining and updates as more data becomes accessible. For detailed information about all the models used in this project, please refer to the model cards available on Hugging Face:
+The models used in this demo are very much a work in progress, and as more data, and new architectures, becomes available, they will be retrained and reevaluated. For more information about the models, please refer to their model-cards on Huggingface.
 
 - [Riksarkivet/rtmdet_regions](https://huggingface.co/Riksarkivet/rtmdet_regions)
 - [Riksarkivet/rtmdet_lines](https://huggingface.co/Riksarkivet/rtmdet_lines)
@@ -15,7 +16,7 @@ The models within this pipeline will be subject to continual retraining and upda
 
 ## Datasets
 
-Both train and evaluation datasets for the models will be released in the future here:
+Train and testsets created by the Swedish National Archives will be released here:
 
 - [Riksarkivet/placeholder_region_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_region_segmentation)
 - [Riksarkivet/placeholder_line_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_line_segmentation)

helper/text/overview/htrflow/htrflow_tab2.md

@@ -1,6 +1,6 @@
 ### Text-region segmentation
 
-To facilitate the text-line segmentation process, it is advantageous to segment the image into text-regions beforehand. This initial step offers several benefits, including reducing variations in line spacing, eliminating blank areas on the page, establishing a clear reading order, and distinguishing marginalia from the main text. The segmentation model utilized in this process predicts both bounding boxes and masks. Although the model has the capability to predict both, only the masks are utilized for the segmentation tasks of lines and regions. An essential post-processing step involves checking for regions that are contained within other regions. During this step, only the containing region is retained, while the contained region is discarded. This ensures that the final segmented text-regions are accurate and devoid of overlapping or redundant areas.
+To facilitate the text-line segmentation process, it is advantageous to segment the image into text-regions beforehand. This initial step offers several benefits, including reducing variations in line spacing, eliminating blank areas on the page, establishing a clear reading order, and distinguishing marginalia from the main text. The segmentation model utilized in this process predicts both bounding boxes and masks. Although the model has the capability to predict both, only the masks are utilized for the segmentation tasks of lines and regions. An essential post-processing step involves checking for regions that are contained within other regions. During this step, only the containing region is retained, while the contained region is discarded. This ensures that the final segmented text-regions are accurate and devoid of overlapping or redundant areas. This ensures that there’s no duplicate text-regions sent to the text-recognition model.
 
 <figure>
 <img src="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/app_project_region.png?raw=true" alt="HTR_tool" style="width:70%; display: block; margin-left: auto; margin-right:auto;" >

helper/text/overview/htrflow/htrflow_tab3.md

@@ -1,6 +1,6 @@
 ### Text-line segmentation
 
-This is also an RTMDet model that's trained on extracting text-lines from cropped text-regions within an image. The same post-processing on the instance segmentation masks is done here as in the text-region segmentation step.
+This is also an instance segmentation model, trained on extracting text-lines from the cropped text-regions. The same post-processing as in the text-region segmentation step, is done in the text-line segmentation step.
 
 <figure>
 <img src="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/app_project_line.png?raw=true" alt="HTR_tool" style="width:70%; display: block; margin-left: auto; margin-right:auto;" >

helper/text/overview/htrflow/htrflow_tab4.md

@@ -1,6 +1,6 @@
-### HTR
+### Text Recognition
 
-For the text-recognition a SATRN model was trained with mmocr on approximately one million handwritten text-line images ranging from 1600 to 1900. It was trained on a wide variety of archival material to make it generalize as well as possible. See below for detailed evaluation results, and also some finetuning experiments.
+The text-recognition model was trained on approximately one million handwritten text-line images ranging from the 17th to the 19th century. See the model card for detailed evaluation results, and results from some fine-tuning experiments.
 
 <figure>
 <img src="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/app_project_htr.png?raw=true" alt="HTR_tool" style="width:70%; display: block; margin-left: auto; margin-right:auto;" >

helper/text/text_overview.py

@@ -17,6 +17,7 @@ class TextOverview:
 
     # Contributions
     contributions = read_markdown("helper/text/overview/contributions/contributions.md")
+    huminfra_image = read_markdown("helper/text/overview/contributions/huminfra_image.md")
 
     # Changelog & Roadmap
     changelog = read_markdown("helper/text/overview/changelog_roadmap/changelog.md")

tabs/overview_tab.py

@@ -23,16 +23,11 @@ with gr.Blocks() as overview:
                     with gr.Tab("Text recognition"):
                         gr.Markdown(TextOverview.htrflow_tab4)
 
-        with gr.Tab("FAQ & Discussion"):
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown(TextOverview.text_faq)
-                with gr.Column():
-                    gr.Markdown(TextOverview.text_discussion)
-
         with gr.Tab("Contributions"):
             with gr.Row():
-                gr.Markdown(TextOverview.contributions)
+                with gr.Column():
+                    gr.Markdown(TextOverview.contributions)
+                    gr.Markdown(TextOverview.huminfra_image)
 
         with gr.Tab("Duplicating for own use & API"):
             with gr.Row():
@@ -63,3 +58,10 @@ with gr.Blocks() as overview:
                     gr.Markdown(TextOverview.changelog)
                 with gr.Column():
                     gr.Markdown(TextOverview.roadmap)
+
+        with gr.Tab("FAQ & Discussion"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown(TextOverview.text_faq)
+                with gr.Column():
+                    gr.Markdown(TextOverview.text_discussion)