GOT-OCR2.0

Achieves OCR-2.0 through a unified end-to-end model.

CommonProductImageOCRDeep Learning

GOT-OCR2.0 is an open-source OCR model aimed at advancing optical character recognition technology towards OCR-2.0 through a unified end-to-end framework. This model supports various OCR tasks, including but not limited to standard text recognition, formatted text recognition, fine-grained OCR, multi-crop OCR, and multi-page OCR. It is based on cutting-edge deep learning techniques, capable of handling complex text recognition scenarios with high accuracy and efficiency.

Supports a variety of OCR tasks
including standard text
formatted text
fine-grained OCR
etc.
Based on deep learning technology
providing high-accuracy text recognition.
Supports OCR processing for multi-page documents.
Offers Huggingface deployment for quick application of the model.
Open-source code

\GOT-OCR2.0 is ideal for enterprises and research institutions that require efficient and accurate text recognition
such as in document digitization
data entry
and office automation. It helps users automate the text recognition process
reducing manual intervention and increasing productivity.\

Used for digitizing historical documents in libraries
automatically converting physical documents into electronic files.
In the financial industry
automating the processing of large volumes of financial statements and contracts.
In the medical field

1. Visit the GitHub page and clone the GOT-OCR2.0 repository to your local machine.
2. Follow the instructions in the README document to install necessary software packages and dependencies.
3. Download and load the model weights from sources like Huggingface
Google Drive
or Baidu Cloud.

Visit

GOT-OCR2.0 Visit Over Time

Monthly Visits

503747431

Bounce Rate

37.31%

Page per Visit

5.7

Visit Duration

00:06:44

GOT-OCR2.0 Visit Trend

GOT-OCR2.0 Visit Geography

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