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A lightweight OCR model developed by PaddlePaddle.
License
Apache 2.0
Commercial use
Downloads
2031
Tasks
Classification
Model Types
ONNX
Model Variants
Name
Version
Available For
Created At
Deploy
RVC2, RVC4
About 2 months ago
Model Details
Model Description
PaddleOCR is a highly efficient and flexible optical character recognition (OCR) system designed for real-world applications. It supports over 80 languages and is optimized for deployment in resource-constrained environments. It achieves high recognition performance on multi-language text, including complex scripts, while remaining efficient for real-time use. It also offers robust capabilities for detecting and recognizing text in challenging conditions such as low-quality images or varying text orientations.
Developed by: PaddlePaddle
Shared by:
Model type: Computer vision
License:
Resources for more information:
Training Details
The model was trained using model distilation from to PPLCNetV3. Training was performed on the dataset.
Testing Details
Metrics
The model was tested on 300 images of different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc.
Model Name
Number of images
F-score
PP-OCRv4
300
0.5224
Technical Specifications
Input/Output Details
Input:
Name: x
Info: 0-255 BGR un-normalized image.
Output:
Name: output
Info: A sequence of 40 characters, each described as classifiction probability over 97 possible symbols. Blank symbol is at index 0 and is removed by default.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
ClassificationSequenceParser that outputs message (classes (letters) and their respective scores).
Get parsed output(s):
while pipeline.isRuning():
parser_output: ClassificationSequenceParser = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
