Context-aware chart extraction that can detect 18 classes for chart basic elements, excluding plot elements.
CACHED (Context-Aware Chart Element Detection) is a state-of-the-art chart element detection model from University at Buffalo. It was published in Document Analysis and Recognition - ICDAR 2023 conference. The code is based on the MMDetection Framework.
CACHED is associated with PaddleOCR to perform Optical Character Recognition (OCR). PaddleOCR is an ultra lightweight OCR system by Baidu.
This model is ready for commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see links to Non-NVIDIA Context-Aware Chart Element Detection GitHub and PaddleOCR Toolkit.
CACHED is licensed under MIT. PaddleOCR is licensed under Apache-2.
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Input Type(s): Image
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: Two Dimensional (2D)
Other Properties Related to Input: Expected input is a nd array image of shape [Channel, Width, Height]
, or a nd array batch of image of shape [Batch, Channel, Width, Height]
.
Output Type(s): Text associated to each of the following classes : "chart_title", "x_title", "y_title", "xlabel", "ylabel", "other", "legend_label", "legend_title", "mark_label", "value_label"
Output Format: Dict of String
Output Parameters: 1D
Other Properties Related to Output: None
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace
None of the models were trained by NVIDIA.
PubMed Central (PMC) Chart Dataset
Text detection and recognition datasets
Engine: Tensor(RT)
Test Hardware: Tested on all supported hardware listed in compatibility section
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