trocr-base-printed

Maintainer: microsoft

Total Score

127

Last updated 5/21/2024

🖼️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The trocr-base-printed model is a Transformer-based Optical Character Recognition (OCR) model fine-tuned on the SROIE dataset. It is an encoder-decoder model, with an image Transformer as the encoder and a text Transformer as the decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from RoBERTa.

The model takes in an image as input and generates the corresponding text. It processes the image by breaking it into a sequence of fixed-size patches, which are then linearly embedded and fed into the Transformer encoder. The decoder then autoregressively generates the output text.

Model inputs and outputs

Inputs

  • Images: The model takes in single text-line images as input, typically of printed text.

Outputs

  • Text: The model generates the corresponding text for the input image.

Capabilities

The trocr-base-printed model is capable of performing accurate optical character recognition (OCR) on printed text-line images. It can be used to extract text from a variety of document types, such as receipts, forms, or other printed materials.

What can I use it for?

You can use the trocr-base-printed model to automate text extraction from documents in your applications or workflows. This could be useful for tasks like invoice processing, data entry, or digitizing physical records. The model's performance on printed text makes it well-suited for industrial or commercial applications that involve processing large volumes of physical documents.

Things to try

One interesting thing to try with the trocr-base-printed model is to experiment with different types of input images, such as handwritten text or more complex document layouts. While the model is primarily designed for printed text, it may still be able to provide useful results in these other domains, albeit with potentially lower accuracy. You can also try fine-tuning the model on your own specialized dataset to improve its performance on your specific use case.



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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The trocr-large-printed model is a large-sized TrOCR (Transformer-based Optical Character Recognition) model that has been fine-tuned on the SROIE dataset. TrOCR is an encoder-decoder model, consisting of an image Transformer as the encoder and a text Transformer as the decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. This model is similar to the trocr-base-printed model, which is a base-sized TrOCR model also fine-tuned on the SROIE dataset. Both models were introduced in the paper "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models" by researchers from Microsoft. Model inputs and outputs Inputs Images**: The model takes single text-line images as input. Outputs Text**: The model generates the text contained in the input image. Capabilities The trocr-large-printed model can be used for optical character recognition (OCR) on single text-line images. By leveraging the power of Transformers, the model can effectively recognize text in images, making it a useful tool for various applications such as document processing, form digitization, and information extraction. What can I use it for? You can use the raw trocr-large-printed model for OCR on single text-line images. Additionally, the model hub offers fine-tuned versions of the TrOCR model that may be better suited for specific tasks or applications that interest you. Things to try One interesting aspect of the TrOCR model is its use of a Transformer-based architecture, which allows it to capture contextual information and dependencies in the input images more effectively than traditional OCR approaches. You could explore how the model performs on a variety of text-line images, including those with different fonts, languages, or formatting, to see how it adapts to different scenarios.

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