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TrOCR

Image-to-Text
PyTorch
LiteRT
android
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TrOCR: Optimized for Mobile Deployment

Transformer based model for state-of-the-art optical character recognition (OCR) on both printed and handwritten text

End-to-end text recognition approach with pre-trained image transformer and text transformer models for both image understanding and wordpiece-level text generation.

This model is an implementation of TrOCR found here.

This repository provides scripts to run TrOCR on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.image_to_text
  • Model Stats:
    • Model checkpoint: trocr-small-stage1
    • Input resolution: 320x320
    • Number of parameters (TrOCRDecoder): 38.3M
    • Model size (TrOCRDecoder) (float): 146 MB
    • Number of parameters (TrOCREncoder): 23.0M
    • Model size (TrOCREncoder) (float): 87.8 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
TrOCRDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 4.222 ms 0 - 72 MB NPU TrOCR.tflite
TrOCRDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 4.012 ms 5 - 66 MB NPU TrOCR.dlc
TrOCRDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 2.632 ms 0 - 132 MB NPU TrOCR.tflite
TrOCRDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 2.82 ms 4 - 130 MB NPU TrOCR.dlc
TrOCRDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.066 ms 0 - 214 MB NPU TrOCR.tflite
TrOCRDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 2.023 ms 1 - 26 MB NPU TrOCR.dlc
TrOCRDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 2.886 ms 0 - 72 MB NPU TrOCR.tflite
TrOCRDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 2.808 ms 5 - 67 MB NPU TrOCR.dlc
TrOCRDecoder float SA7255P ADP Qualcomm® SA7255P TFLITE 4.222 ms 0 - 72 MB NPU TrOCR.tflite
TrOCRDecoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 4.012 ms 5 - 66 MB NPU TrOCR.dlc
TrOCRDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.081 ms 0 - 215 MB NPU TrOCR.tflite
TrOCRDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 2.013 ms 1 - 26 MB NPU TrOCR.dlc
TrOCRDecoder float SA8295P ADP Qualcomm® SA8295P TFLITE 3.157 ms 0 - 64 MB NPU TrOCR.tflite
TrOCRDecoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 2.997 ms 0 - 58 MB NPU TrOCR.dlc
TrOCRDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.06 ms 0 - 224 MB NPU TrOCR.tflite
TrOCRDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 2.093 ms 1 - 27 MB NPU TrOCR.dlc
TrOCRDecoder float SA8775P ADP Qualcomm® SA8775P TFLITE 2.886 ms 0 - 72 MB NPU TrOCR.tflite
TrOCRDecoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 2.808 ms 5 - 67 MB NPU TrOCR.dlc
TrOCRDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.114 ms 0 - 244 MB NPU TrOCR.tflite
TrOCRDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 2.082 ms 2 - 26 MB NPU TrOCR.dlc
TrOCRDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 2.561 ms 0 - 178 MB NPU TrOCR.onnx.zip
TrOCRDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.541 ms 0 - 148 MB NPU TrOCR.tflite
TrOCRDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 1.464 ms 0 - 141 MB NPU TrOCR.dlc
TrOCRDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.847 ms 0 - 144 MB NPU TrOCR.onnx.zip
TrOCRDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.399 ms 0 - 73 MB NPU TrOCR.tflite
TrOCRDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 1.257 ms 2 - 153 MB NPU TrOCR.dlc
TrOCRDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 1.565 ms 1 - 155 MB NPU TrOCR.onnx.zip
TrOCRDecoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 2.151 ms 654 - 654 MB NPU TrOCR.dlc
TrOCRDecoder float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.325 ms 68 - 68 MB NPU TrOCR.onnx.zip
TrOCREncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 48.039 ms 7 - 115 MB NPU TrOCR.tflite
TrOCREncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 46.212 ms 2 - 139 MB NPU TrOCR.dlc
TrOCREncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 25.412 ms 7 - 128 MB NPU TrOCR.tflite
TrOCREncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 28.476 ms 1 - 130 MB NPU TrOCR.dlc
TrOCREncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 17.742 ms 7 - 23 MB NPU TrOCR.tflite
TrOCREncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 17.251 ms 2 - 23 MB NPU TrOCR.dlc
TrOCREncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 20.329 ms 3 - 111 MB NPU TrOCR.tflite
TrOCREncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 19.843 ms 2 - 137 MB NPU TrOCR.dlc
TrOCREncoder float SA7255P ADP Qualcomm® SA7255P TFLITE 48.039 ms 7 - 115 MB NPU TrOCR.tflite
TrOCREncoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 46.212 ms 2 - 139 MB NPU TrOCR.dlc
TrOCREncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 17.733 ms 7 - 26 MB NPU TrOCR.tflite
TrOCREncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 17.376 ms 2 - 26 MB NPU TrOCR.dlc
TrOCREncoder float SA8295P ADP Qualcomm® SA8295P TFLITE 28.15 ms 7 - 119 MB NPU TrOCR.tflite
TrOCREncoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 25.234 ms 2 - 126 MB NPU TrOCR.dlc
TrOCREncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 17.794 ms 7 - 24 MB NPU TrOCR.tflite
TrOCREncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 17.314 ms 2 - 24 MB NPU TrOCR.dlc
TrOCREncoder float SA8775P ADP Qualcomm® SA8775P TFLITE 20.329 ms 3 - 111 MB NPU TrOCR.tflite
TrOCREncoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 19.843 ms 2 - 137 MB NPU TrOCR.dlc
TrOCREncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 17.804 ms 7 - 25 MB NPU TrOCR.tflite
TrOCREncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 17.267 ms 2 - 26 MB NPU TrOCR.dlc
TrOCREncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 39.748 ms 0 - 145 MB NPU TrOCR.onnx.zip
TrOCREncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 12.3 ms 6 - 117 MB NPU TrOCR.tflite
TrOCREncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 11.676 ms 2 - 146 MB NPU TrOCR.dlc
TrOCREncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 31.985 ms 15 - 173 MB NPU TrOCR.onnx.zip
TrOCREncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 10.719 ms 6 - 118 MB NPU TrOCR.tflite
TrOCREncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 10.774 ms 2 - 157 MB NPU TrOCR.dlc
TrOCREncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 27.188 ms 15 - 198 MB NPU TrOCR.onnx.zip
TrOCREncoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 18.031 ms 211 - 211 MB NPU TrOCR.dlc
TrOCREncoder float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 37.09 ms 50 - 50 MB NPU TrOCR.onnx.zip

Installation

Install the package via pip:

pip install "qai-hub-models[trocr]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.trocr.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.trocr.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.trocr.export

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.trocr import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on TrOCR's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of TrOCR can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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