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EasyOCR

Image-to-Text
PyTorch
LiteRT
ONNX
android
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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
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
EasyOCRDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 41.189 ms 0 - 136 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 39.017 ms 6 - 9 MB FP16 NPU EasyOCR.so
EasyOCRDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 40.015 ms 34 - 181 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 30.181 ms 14 - 45 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 29.323 ms 6 - 25 MB FP16 NPU EasyOCR.so
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 29.584 ms 38 - 75 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 28.753 ms 15 - 45 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 24.26 ms 6 - 36 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 28.097 ms 43 - 78 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector SA7255P ADP SA7255P TFLITE 2113.678 ms 3 - 28 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA7255P ADP SA7255P QNN 2111.684 ms 0 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector SA8255 (Proxy) SA8255P Proxy TFLITE 41.731 ms 0 - 97 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8255 (Proxy) SA8255P Proxy QNN 38.998 ms 6 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector SA8295P ADP SA8295P TFLITE 78.45 ms 16 - 42 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8295P ADP SA8295P QNN 76.549 ms 0 - 11 MB FP16 NPU Use Export Script
EasyOCRDetector SA8650 (Proxy) SA8650P Proxy TFLITE 42.824 ms 0 - 145 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8650 (Proxy) SA8650P Proxy QNN 40.764 ms 6 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector SA8775P ADP SA8775P TFLITE 88.536 ms 16 - 41 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8775P ADP SA8775P QNN 86.522 ms 1 - 9 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8275 (Proxy) QCS8275 Proxy TFLITE 2113.678 ms 3 - 28 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS8275 (Proxy) QCS8275 Proxy QNN 2111.684 ms 0 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8550 (Proxy) QCS8550 Proxy TFLITE 41.678 ms 0 - 126 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS8550 (Proxy) QCS8550 Proxy QNN 39.278 ms 6 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector QCS9075 (Proxy) QCS9075 Proxy TFLITE 88.536 ms 16 - 41 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS9075 (Proxy) QCS9075 Proxy QNN 86.522 ms 1 - 9 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8450 (Proxy) QCS8450 Proxy TFLITE 80.295 ms 16 - 48 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS8450 (Proxy) QCS8450 Proxy QNN 69.9 ms 6 - 37 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon X Elite CRD Snapdragon® X Elite QNN 39.87 ms 6 - 6 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon X Elite CRD Snapdragon® X Elite ONNX 41.319 ms 66 - 66 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 109.812 ms 6 - 8 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 20.483 ms 0 - 3 MB FP16 NPU EasyOCR.so
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 21.731 ms 0 - 24 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 108.852 ms 2 - 20 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 14.237 ms 0 - 16 MB FP16 NPU EasyOCR.so
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 16.212 ms 1 - 24 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 107.149 ms 14 - 30 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 20.63 ms 0 - 346 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 17.677 ms 0 - 18 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer SA7255P ADP SA7255P TFLITE 565.404 ms 9 - 17 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA7255P ADP SA7255P QNN 285.155 ms 0 - 8 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8255 (Proxy) SA8255P Proxy TFLITE 124.344 ms 9 - 11 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8255 (Proxy) SA8255P Proxy QNN 20.321 ms 0 - 3 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8295P ADP SA8295P TFLITE 214.709 ms 8 - 18 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8295P ADP SA8295P QNN 30.834 ms 0 - 12 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8650 (Proxy) SA8650P Proxy TFLITE 101.784 ms 7 - 11 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8650 (Proxy) SA8650P Proxy QNN 20.407 ms 0 - 3 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8775P ADP SA8775P TFLITE 415.153 ms 6 - 14 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8775P ADP SA8775P QNN 29.021 ms 0 - 7 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8275 (Proxy) QCS8275 Proxy TFLITE 565.404 ms 9 - 17 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8275 (Proxy) QCS8275 Proxy QNN 285.155 ms 0 - 8 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8550 (Proxy) QCS8550 Proxy TFLITE 108.193 ms 7 - 10 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8550 (Proxy) QCS8550 Proxy QNN 20.315 ms 0 - 3 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS9075 (Proxy) QCS9075 Proxy TFLITE 415.153 ms 6 - 14 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS9075 (Proxy) QCS9075 Proxy QNN 29.021 ms 0 - 7 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8450 (Proxy) QCS8450 Proxy TFLITE 210.333 ms 9 - 25 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8450 (Proxy) QCS8450 Proxy QNN 34.309 ms 0 - 151 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon X Elite CRD Snapdragon® X Elite QNN 21.364 ms 0 - 0 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon X Elite CRD Snapdragon® X Elite ONNX 19.37 ms 0 - 0 MB FP16 NPU EasyOCR.onnx

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

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