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TrOCR

Image-to-Text
PyTorch
TF Lite
ONNX
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: Image to text
  • Model Stats:
    • Model checkpoint: trocr-small-stage1
    • Input resolution: 320x320
    • Number of parameters (TrOCREncoder): 23.0M
    • Model size (TrOCREncoder): 87.8 MB
    • Number of parameters (TrOCRDecoder): 38.3M
    • Model size (TrOCRDecoder): 146 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
TrOCRDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 2.203 ms 0 - 143 MB FP16 NPU TrOCR.tflite
TrOCRDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 2.367 ms 2 - 355 MB FP16 NPU TrOCR.so
TrOCRDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 2.762 ms 1 - 3 MB FP16 NPU TrOCR.onnx
TrOCRDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 1.561 ms 0 - 48 MB FP16 NPU TrOCR.tflite
TrOCRDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 1.729 ms 0 - 51 MB FP16 NPU TrOCR.so
TrOCRDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 2.1 ms 0 - 174 MB FP16 NPU TrOCR.onnx
TrOCRDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 1.451 ms 0 - 46 MB FP16 NPU TrOCR.tflite
TrOCRDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 1.545 ms 0 - 46 MB FP16 NPU Use Export Script
TrOCRDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 1.88 ms 0 - 132 MB FP16 NPU TrOCR.onnx
TrOCRDecoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 2.205 ms 0 - 364 MB FP16 NPU TrOCR.tflite
TrOCRDecoder QCS8550 (Proxy) QCS8550 Proxy QNN 2.25 ms 0 - 1 MB FP16 NPU Use Export Script
TrOCRDecoder SA7255P ADP SA7255P TFLITE 12.302 ms 0 - 44 MB FP16 NPU TrOCR.tflite
TrOCRDecoder SA7255P ADP SA7255P QNN 12.414 ms 7 - 17 MB FP16 NPU Use Export Script
TrOCRDecoder SA8255 (Proxy) SA8255P Proxy TFLITE 2.21 ms 0 - 87 MB FP16 NPU TrOCR.tflite
TrOCRDecoder SA8255 (Proxy) SA8255P Proxy QNN 2.316 ms 1 - 2 MB FP16 NPU Use Export Script
TrOCRDecoder SA8295P ADP SA8295P TFLITE 3.067 ms 0 - 42 MB FP16 NPU TrOCR.tflite
TrOCRDecoder SA8295P ADP SA8295P QNN 3.74 ms 7 - 13 MB FP16 NPU Use Export Script
TrOCRDecoder SA8650 (Proxy) SA8650P Proxy TFLITE 2.27 ms 0 - 346 MB FP16 NPU TrOCR.tflite
TrOCRDecoder SA8650 (Proxy) SA8650P Proxy QNN 2.35 ms 2 - 4 MB FP16 NPU Use Export Script
TrOCRDecoder SA8775P ADP SA8775P TFLITE 3.341 ms 0 - 45 MB FP16 NPU TrOCR.tflite
TrOCRDecoder SA8775P ADP SA8775P QNN 3.578 ms 7 - 13 MB FP16 NPU Use Export Script
TrOCRDecoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 2.671 ms 0 - 48 MB FP16 NPU TrOCR.tflite
TrOCRDecoder QCS8450 (Proxy) QCS8450 Proxy QNN 2.74 ms 4 - 56 MB FP16 NPU Use Export Script
TrOCRDecoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.444 ms 7 - 7 MB FP16 NPU Use Export Script
TrOCRDecoder Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.741 ms 69 - 69 MB FP16 NPU TrOCR.onnx
TrOCREncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 50.015 ms 7 - 34 MB FP16 NPU TrOCR.tflite
TrOCREncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 53.281 ms 2 - 22 MB FP16 NPU TrOCR.so
TrOCREncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 38.056 ms 0 - 57 MB FP16 NPU TrOCR.onnx
TrOCREncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 39.322 ms 5 - 67 MB FP16 NPU TrOCR.tflite
TrOCREncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 41.406 ms 2 - 61 MB FP16 NPU TrOCR.so
TrOCREncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 31.095 ms 0 - 259 MB FP16 NPU TrOCR.onnx
TrOCREncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 36.222 ms 5 - 68 MB FP16 NPU TrOCR.tflite
TrOCREncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 33.821 ms 2 - 66 MB FP16 NPU Use Export Script
TrOCREncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 24.497 ms 16 - 138 MB FP16 NPU TrOCR.onnx
TrOCREncoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 49.833 ms 7 - 32 MB FP16 NPU TrOCR.tflite
TrOCREncoder QCS8550 (Proxy) QCS8550 Proxy QNN 36.818 ms 2 - 8 MB FP16 NPU Use Export Script
TrOCREncoder SA7255P ADP SA7255P TFLITE 266.53 ms 7 - 69 MB FP16 NPU TrOCR.tflite
TrOCREncoder SA7255P ADP SA7255P QNN 247.644 ms 2 - 12 MB FP16 NPU Use Export Script
TrOCREncoder SA8255 (Proxy) SA8255P Proxy TFLITE 50.253 ms 7 - 30 MB FP16 NPU TrOCR.tflite
TrOCREncoder SA8255 (Proxy) SA8255P Proxy QNN 37.723 ms 2 - 4 MB FP16 NPU Use Export Script
TrOCREncoder SA8295P ADP SA8295P QNN 50.866 ms 4 - 10 MB FP16 NPU Use Export Script
TrOCREncoder SA8650 (Proxy) SA8650P Proxy TFLITE 50.307 ms 7 - 34 MB FP16 NPU TrOCR.tflite
TrOCREncoder SA8650 (Proxy) SA8650P Proxy QNN 37.01 ms 2 - 3 MB FP16 NPU Use Export Script
TrOCREncoder SA8775P ADP SA8775P TFLITE 59.803 ms 7 - 69 MB FP16 NPU TrOCR.tflite
TrOCREncoder SA8775P ADP SA8775P QNN 42.412 ms 2 - 8 MB FP16 NPU Use Export Script
TrOCREncoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 60.304 ms 7 - 69 MB FP16 NPU TrOCR.tflite
TrOCREncoder QCS8450 (Proxy) QCS8450 Proxy QNN 63.0 ms 0 - 64 MB FP16 NPU Use Export Script
TrOCREncoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 34.029 ms 2 - 2 MB FP16 NPU Use Export Script
TrOCREncoder Snapdragon X Elite CRD Snapdragon® X Elite ONNX 36.913 ms 49 - 49 MB FP16 NPU TrOCR.onnx

Installation

This model can be installed as a Python 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

Profiling Results
------------------------------------------------------------
TrOCRDecoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 2.2                    
Estimated peak memory usage (MB): [0, 143]               
Total # Ops                     : 399                    
Compute Unit(s)                 : NPU (399 ops)          

------------------------------------------------------------
TrOCREncoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 50.0                   
Estimated peak memory usage (MB): [7, 34]                
Total # Ops                     : 591                    
Compute Unit(s)                 : NPU (591 ops)

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
model = Model.from_pretrained()
decoder_model = model.decoder
encoder_model = model.encoder

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
decoder_input_shape = decoder_model.get_input_spec()
decoder_sample_inputs = decoder_model.sample_inputs()

traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()])

# Compile model on a specific device
decoder_compile_job = hub.submit_compile_job(
    model=traced_decoder_model ,
    device=device,
    input_specs=decoder_model.get_input_spec(),
)

# Get target model to run on-device
decoder_target_model = decoder_compile_job.get_target_model()
# Trace model
encoder_input_shape = encoder_model.get_input_spec()
encoder_sample_inputs = encoder_model.sample_inputs()

traced_encoder_model = torch.jit.trace(encoder_model, [torch.tensor(data[0]) for _, data in encoder_sample_inputs.items()])

# Compile model on a specific device
encoder_compile_job = hub.submit_compile_job(
    model=traced_encoder_model ,
    device=device,
    input_specs=encoder_model.get_input_spec(),
)

# Get target model to run on-device
encoder_target_model = encoder_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.

decoder_profile_job = hub.submit_profile_job(
    model=decoder_target_model,
    device=device,
)
encoder_profile_job = hub.submit_profile_job(
    model=encoder_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.

decoder_input_data = decoder_model.sample_inputs()
decoder_inference_job = hub.submit_inference_job(
    model=decoder_target_model,
    device=device,
    inputs=decoder_input_data,
)
decoder_inference_job.download_output_data()
encoder_input_data = encoder_model.sample_inputs()
encoder_inference_job = hub.submit_inference_job(
    model=encoder_target_model,
    device=device,
    inputs=encoder_input_data,
)
encoder_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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