qualcomm / EasyOCR

EasyOCR: Optimized for Mobile Deployment

Ready-to-use OCR with 80+ supported languages and all popular writing scripts

EasyOCR is a machine learning model that can recognize text in images. It supports 80+ supported languages and all popular writing scripts.

This model is an implementation of EasyOCR found here .

This repository provides scripts to run EasyOCR 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: easyocr-small-stage1
  • Input resolution: 384x384
  • Number of parameters (EasyOCRDetector): 20.8M
  • Model size (EasyOCRDetector): 79.2 MB
  • Number of parameters (EasyOCRRecognizer): 3.84M
  • Model size (EasyOCRRecognizer): 14.7 MB

Installation

Install the package via pip:

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

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.easyocr.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.easyocr.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.easyocr.export

Profiling Results
------------------------------------------------------------
EasyOCRDetector
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 41.2                   
Estimated peak memory usage (MB): [0, 136]               
Total # Ops                     : 42                     
Compute Unit(s)                 : NPU (42 ops)           

------------------------------------------------------------
EasyOCRRecognizer
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 109.8                  
Estimated peak memory usage (MB): [6, 8]                 
Total # Ops                     : 136                    
Compute Unit(s)                 : CPU (136 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.easyocr import Model

# Load the model
model = Model.from_pretrained()
detector_model = model.detector
recognizer_model = model.recognizer

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

# Trace model
detector_input_shape = detector_model.get_input_spec()
detector_sample_inputs = detector_model.sample_inputs()

traced_detector_model = torch.jit.trace(detector_model, [torch.tensor(data[0]) for _, data in detector_sample_inputs.items()])

# Compile model on a specific device
detector_compile_job = hub.submit_compile_job(
    model=traced_detector_model ,
    device=device,
    input_specs=detector_model.get_input_spec(),
)

# Get target model to run on-device
detector_target_model = detector_compile_job.get_target_model()
# Trace model
recognizer_input_shape = recognizer_model.get_input_spec()
recognizer_sample_inputs = recognizer_model.sample_inputs()

traced_recognizer_model = torch.jit.trace(recognizer_model, [torch.tensor(data[0]) for _, data in recognizer_sample_inputs.items()])

# Compile model on a specific device
recognizer_compile_job = hub.submit_compile_job(
    model=traced_recognizer_model ,
    device=device,
    input_specs=recognizer_model.get_input_spec(),
)

# Get target model to run on-device
recognizer_target_model = recognizer_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.

detector_profile_job = hub.submit_profile_job(
    model=detector_target_model,
    device=device,
)
recognizer_profile_job = hub.submit_profile_job(
    model=recognizer_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.

detector_input_data = detector_model.sample_inputs()
detector_inference_job = hub.submit_inference_job(
    model=detector_target_model,
    device=device,
    inputs=detector_input_data,
)
detector_inference_job.download_output_data()
recognizer_input_data = recognizer_model.sample_inputs()
recognizer_inference_job = hub.submit_inference_job(
    model=recognizer_target_model,
    device=device,
    inputs=recognizer_input_data,
)
recognizer_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 EasyOCR's performance across various devices here . Explore all available models on Qualcomm® AI Hub

License

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

References

• None
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us .