Create README.md

README.md

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+---
+license: mit
+language:
+- sv
+pipeline_tag: image-to-text
+tags:
+- HTR
+---
+# Model Card: Swedish National Archives HTR Pipeline
+
+## Model Description
+The Swedish National Archives presents an end-to-end Handwritten Text Recognition (HTR) pipeline for running-text documents ranging from 1600 to 1900. The pipeline consists of the following components:
+
+1. **RTMDet Instance Segmentation Models**: The pipeline utilizes two RTMDet instance segmentation models, trained using MMDetection. The first model is designed to segment text regions within the documents, while the second model focuses on segmenting text lines within these regions. These models enable the identification and localization of text areas, which is a crucial step in the HTR pipeline.
+
+2. **SATRN HTR Model**: The pipeline incorporates a SATRN (Spatial Attention Transformer Networks) model, trained using MMOCR (OpenMMLab's OCR toolbox). SATRN is a state-of-the-art model for HTR tasks and provides accurate recognition of handwritten text. The SATRN model is trained specifically to handle the characteristics and challenges of handwritten text present in the Swedish National Archives' documents.
+
+The models are designed to provide a generic pipeline for handwritten text recognition, offering robust performance for documents from the years 1600 to 1900.
+
+## Intended Use
+The Swedish National Archives HTR pipeline is intended to be used for the following purposes:
+
+- Handwritten Text Recognition: The pipeline enables the automatic recognition of handwritten text in running-text documents from the 17th to the 19th century. It can be utilized by researchers, historians, and archivists to efficiently transcribe and analyze historical texts.
+
+- Document Digitization: The pipeline aids in the process of digitizing archival documents by automating the extraction and transcription of handwritten text. This facilitates broader accessibility and preservation of historical materials.
+
+It's important to note that the pipeline is optimized for running-text documents from the specified time period and may not perform optimally for other types of documents or handwriting styles.
+
+## Performance and Limitations
+The performance of the Swedish National Archives HTR pipeline is influenced by several factors:
+
+- **Accuracy**: The pipeline achieves high accuracy in segmenting text regions and lines, as well as recognizing the text content accurately. However, the recognition accuracy may vary depending on the quality of the original document, handwriting style, and legibility.
+
+- **Speed**: The pipeline aims to provide real-time or near real-time performance for efficient processing of handwritten text documents. The speed may vary depending on the hardware used for inference.
+
+- **Document Specificity**: The pipeline is specifically trained for running-text documents from the 17th to the 19th century. It may not perform optimally for documents outside this time range or for documents with unique characteristics or handwriting styles not covered by the training data.
+
+- **Language Limitations**: The pipeline is tailored for Swedish text recognition. While it may handle other languages to some extent, its performance may not be as accurate as for Swedish.
+
+- **Handwriting Style**: The pipeline is optimized for the cursive handwriting style prevalent in the historical documents of the Swedish National Archives. It may not perform as well for other handwriting styles, such as block letters or highly stylized scripts.
+
+## Training Data
+The Swedish National Archives HTR pipeline was trained using a diverse dataset of running-text documents from the 17th to the 19th century. The training data includes various types of historical texts, such as letters, manuscripts, and official records.
+
+The dataset comprises both high-quality and challenging examples to ensure the models' robustness. It covers a wide range of handwriting styles, legibility levels, and document conditions.
+
+The training data was annotated to provide ground truth for text region and line segmentation, as well as text transcription. Expert archivists and historians contributed to the annotation process to ensure accurate labeling.
+
+## Ethical Considerations
+When utilizing the Swedish National Archives HTR pipeline, it is essential to be mindful of the following ethical considerations:
+
+- **Privacy**: Care should be taken to ensure that the pipeline is used in compliance with relevant privacy laws and regulations. If the documents contain sensitive or personally identifiable information, appropriate measures must be taken to protect individuals' privacy.
+
+- **Bias and Fairness**: The pipeline's performance may vary across different demographic groups, handwriting styles, or historical periods. It's crucial to assess and address any biases that may arise during the model's deployment and usage.
+
+- **Historical Context**: Historical documents may contain sensitive or offensive content. It is the user's responsibility to interpret and contextualize the results generated by the pipeline while considering the historical context and potential societal impact.
+
+## Caveats and Future Work
+Although the Swedish National Archives HTR pipeline has been trained and optimized for running-text documents from the specified time period, there are a few caveats and considerations to keep in mind:
+
+- **Out-of-Scope Documents**: The pipeline may encounter difficulties when processing documents that deviate significantly from the expected document characteristics or handwriting styles present in the training data.
+
+- **Continuous Improvement**: The pipeline can benefit from continuous updates and improvements as new training data becomes available and advancements in OCR technology occur. Regular evaluations and updates are recommended to enhance its performance and adaptability.
+
+- **User Feedback**: Users are encouraged to provide feedback on the pipeline's performance, identify issues, and report any potential biases or limitations. This feedback can contribute to refining the pipeline and addressing concerns.
+
+## Conclusion
+The Swedish National Archives HTR pipeline offers a powerful solution for automatic handwritten text recognition in running-text documents from the 17th to the 19th century. It leverages instance segmentation models for text region and line extraction, combined with a state-of-the-art SATRN model for accurate recognition. While the pipeline demonstrates high accuracy, speed, and flexibility, users should be aware of its limitations and consider the ethical implications of using historical data. Regular updates and user feedback are crucial for maintaining and improving the pipeline's performance.
+
+For further inquiries or assistance, please reach out to the Swedish National Archives.
+
+## References
+If you would like to learn more about the Swedish National Archives HTR pipeline or access the training data, please refer to the following resources:
+
+- [Swedish National Archives](https://www.riksarkivet.se/)
+- [MMDetection](https://github.com/open-mmlab/mmdetection)
+- [MMOCR](https://github.com/open-mmlab/mmocr)
+- [SATRN Paper](https://arxiv.org/abs/2012.05483)
+- [OpenMMLab OCR Toolbox](https://openmmlab.com/mmocr/)