GOT-OCR2_0
Maintainer: ucaslcl
332
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The GOT-OCR2_0 model, created by maintainer ucaslcl, is an end-to-end optical character recognition (OCR) model that can handle a wide range of text formats, including plain text, formatted text, fine-grained OCR, and multi-crop OCR. This model is an advancement in OCR technology, building upon the previous "OCR 1.0" approaches by providing a more unified and robust solution.
The GOT-OCR2_0 model is trained on a large dataset of cultural heritage archives, allowing it to accurately recognize and correct text from historical documents. It can handle a variety of input types, including images with noisy or degraded text, and provides high-quality output in markdown format. The model's capabilities are highlighted in its strong performance on benchmarks like TextVQA, DocVQA, ChartQA, and OCRbench, where it outperforms other open-source and commercial models.
Model inputs and outputs
Inputs
- Image file: The model takes an image file as input, which can contain text in various formats, such as plain text, formatted text, or a mixture of text and other elements.
Outputs
- Markdown-formatted text: The model's primary output is the text content of the input image, formatted in Markdown syntax. This includes:
- Detected text, with headers marked by
## - Mathematical expressions wrapped in
\( inline math \)and\[ display math \] - Formatting elements like bold, italic, and code blocks
- Detected text, with headers marked by
The model can also provide additional outputs, such as:
- Fine-grained OCR: Bounding boxes and text annotations for individual text elements in the image.
- Multi-crop OCR: Detection and recognition of multiple text regions within the input image.
- Rendered HTML: The formatted text output can be rendered as an HTML document for easy visualization.
Capabilities
The GOT-OCR2_0 model excels at handling a wide range of text formats, including plain text, formatted text, mathematical expressions, and mixed-content documents. It can accurately detect and recognize text, even in noisy or degraded images, and provide high-quality Markdown-formatted output.
One of the key strengths of the GOT-OCR2_0 model is its ability to handle historical documents. Thanks to its training on a large dataset of cultural heritage archives, the model can accurately recognize and correct text from old, damaged, or low-quality sources. This makes it a valuable tool for researchers and archivists working with historical documents.
What can I use it for?
The GOT-OCR2_0 model is well-suited for a variety of applications, including:
- Document digitization and archiving: Convert physical documents into searchable, structured digital formats, making it easier to preserve and access historical records.
- Automated data extraction: Extract structured data from scanned forms, invoices, or other business documents, reducing manual data entry tasks.
- Assistive technology: Improve accessibility by providing accurate text recognition for people with visual impairments or other disabilities.
- Academic and research applications: Enhance text analysis and information retrieval tasks for historical, scientific, or other specialized domains.
Things to try
One interesting application of the GOT-OCR2_0 model is its ability to handle mathematical expressions. By wrapping detected equations in Markdown syntax, the model makes it easier to process and analyze the mathematical content of documents. This could be particularly useful for researchers in fields like physics, engineering, or finance, where accurate extraction of formulas and equations is crucial.
Another area to explore is the model's fine-grained OCR capabilities. By providing bounding boxes and text annotations for individual elements, the GOT-OCR2_0 model can enable more advanced document analysis, such as layout reconstruction, table extraction, or figure captioning. This could be valuable for applications like automated document processing or information retrieval.
Overall, the GOT-OCR2_0 model represents a significant advancement in OCR technology, delivering robust and versatile text recognition capabilities that can benefit a wide range of industries and applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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