GOT-OCR2_0
Maintainer: stepfun-ai
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, developed by stepfun-ai, is a powerful and versatile optical character recognition (OCR) system that can handle a wide range of text formats, including plain text, formatted text, and even fine-grained OCR with bounding boxes and color information. This model is an upgrade to the previous GOT-OCR model, addressing key issues and enhancing its capabilities.
The GOT-OCR2_0 model is built upon the Hugging Face Transformers library and can be used with NVIDIA GPUs for efficient inference. It is capable of performing various OCR-related tasks, from extracting plain text from images to generating formatted output with layout and styling information. The model's flexibility allows users to customize the level of detail in the OCR results, making it suitable for a variety of applications.
Similar models such as the GOT-OCR2_0 and kivotos-xl-2.0 have also been developed for image-to-text conversion and text understanding tasks, each with its own unique capabilities and use cases.
Model inputs and outputs
Inputs
- Image file: The
GOT-OCR2_0model takes an image file as input, which can be in various formats such as JPEG, PNG, or BMP.
Outputs
- Plain text OCR: The model can extract plain text from the input image and return the recognized text.
- Formatted text OCR: The model can generate formatted text output, including information about the layout and styling of the text, such as bounding boxes, line breaks, and font colors.
- Fine-grained OCR: The model can provide detailed information about the text, including bounding boxes and color information, enabling more advanced text processing and layout analysis.
- Multi-crop OCR: The model can handle multiple cropped regions in the input image and generate OCR results for each of them.
Capabilities
The GOT-OCR2_0 model excels at accurately extracting text from a wide range of image types, including scanned documents, screenshots, and photographs. It can handle both simple and complex layouts, and its ability to recognize formatted text and fine-grained details sets it apart from traditional OCR solutions.
One of the key capabilities of this model is its versatility. It can be used for a variety of applications, such as converting physical documents into editable digital formats, automating data entry processes, and enhancing document management systems. The model's flexibility also makes it suitable for use in industries like publishing, legal, and financial services, where accurate text extraction and layout preservation are crucial.
What can I use it for?
The GOT-OCR2_0 model can be a valuable tool for a wide range of applications that involve text extraction and processing from images. Some potential use cases include:
- Document digitization: Converting physical documents, such as forms, contracts, or books, into searchable and editable digital formats.
- Workflow automation: Streamlining data entry processes by automating the extraction of relevant information from documents.
- Content management: Enhancing document management systems by enabling the extraction and preservation of text layout and formatting.
- Research and analysis: Extracting text from images for further processing, such as natural language processing or data analysis.
Things to try
One interesting aspect of the GOT-OCR2_0 model is its ability to handle fine-grained OCR, which includes the extraction of bounding boxes and color information. This feature can be particularly useful for applications that require precise layout and formatting preservation, such as in the publishing or legal industries.
Another interesting aspect is the model's multi-crop OCR capability, which allows it to handle multiple text-containing regions within a single image. This can be beneficial for processing complex documents or images with multiple text elements, such as forms or technical diagrams.
To explore the full capabilities of the GOT-OCR2_0 model, you can try experimenting with different input images, testing the various OCR types (plain text, formatted text, fine-grained, and multi-crop), and evaluating the quality and accuracy of the results. The model's versatility and customization options make it a powerful tool for a wide range of text extraction and processing tasks.
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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