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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 41.72 ms 1 - 132 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 37.7 ms 6 - 17 MB FP16 NPU EasyOCR.so
EasyOCRDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 41.887 ms 32 - 121 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 30.07 ms 14 - 72 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 28.085 ms 6 - 24 MB FP16 NPU EasyOCR.so
EasyOCRDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 30.704 ms 42 - 74 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 29.358 ms 14 - 48 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 28.786 ms 6 - 33 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 24.189 ms 40 - 68 MB FP16 NPU EasyOCR.onnx
EasyOCRDetector SA7255P ADP SA7255P TFLITE 2113.984 ms 0 - 30 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA7255P ADP SA7255P QNN 2109.673 ms 1 - 10 MB FP16 NPU Use Export Script
EasyOCRDetector SA8255 (Proxy) SA8255P Proxy TFLITE 41.306 ms 9 - 140 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8255 (Proxy) SA8255P Proxy QNN 38.731 ms 6 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector SA8295P ADP SA8295P TFLITE 78.453 ms 16 - 49 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8295P ADP SA8295P QNN 75.057 ms 0 - 18 MB FP16 NPU Use Export Script
EasyOCRDetector SA8650 (Proxy) SA8650P Proxy TFLITE 41.546 ms 10 - 144 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8650 (Proxy) SA8650P Proxy QNN 38.334 ms 6 - 7 MB FP16 NPU Use Export Script
EasyOCRDetector SA8775P ADP SA8775P TFLITE 88.531 ms 16 - 45 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector SA8775P ADP SA8775P QNN 84.933 ms 1 - 11 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8275 (Proxy) QCS8275 Proxy TFLITE 2113.984 ms 0 - 30 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS8275 (Proxy) QCS8275 Proxy QNN 2109.673 ms 1 - 10 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8550 (Proxy) QCS8550 Proxy QNN 38.797 ms 6 - 8 MB FP16 NPU Use Export Script
EasyOCRDetector QCS9075 (Proxy) QCS9075 Proxy TFLITE 88.531 ms 16 - 45 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS9075 (Proxy) QCS9075 Proxy QNN 84.933 ms 1 - 11 MB FP16 NPU Use Export Script
EasyOCRDetector QCS8450 (Proxy) QCS8450 Proxy TFLITE 82.831 ms 16 - 77 MB FP16 NPU EasyOCR.tflite
EasyOCRDetector QCS8450 (Proxy) QCS8450 Proxy QNN 69.737 ms 6 - 36 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon X Elite CRD Snapdragon® X Elite QNN 38.608 ms 6 - 6 MB FP16 NPU Use Export Script
EasyOCRDetector Snapdragon X Elite CRD Snapdragon® X Elite ONNX 41.643 ms 66 - 66 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 117.587 ms 3 - 6 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 23.252 ms 0 - 3 MB FP16 NPU EasyOCR.so
EasyOCRRecognizer Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 20.753 ms 0 - 22 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 109.883 ms 9 - 29 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 16.886 ms 0 - 18 MB FP16 NPU EasyOCR.so
EasyOCRRecognizer Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 16.816 ms 0 - 25 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 106.439 ms 20 - 35 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 19.025 ms 0 - 428 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 14.032 ms 0 - 22 MB FP16 NPU EasyOCR.onnx
EasyOCRRecognizer SA7255P ADP SA7255P TFLITE 571.291 ms 8 - 18 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA7255P ADP SA7255P QNN 281.893 ms 0 - 10 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8255 (Proxy) SA8255P Proxy TFLITE 129.985 ms 2 - 4 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8255 (Proxy) SA8255P Proxy QNN 23.373 ms 0 - 2 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8295P ADP SA8295P TFLITE 218.62 ms 6 - 23 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8295P ADP SA8295P QNN 39.157 ms 0 - 18 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8650 (Proxy) SA8650P Proxy TFLITE 125.209 ms 7 - 10 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8650 (Proxy) SA8650P Proxy QNN 23.218 ms 0 - 3 MB FP16 NPU Use Export Script
EasyOCRRecognizer SA8775P ADP SA8775P TFLITE 410.407 ms 11 - 21 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer SA8775P ADP SA8775P QNN 31.266 ms 0 - 10 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8275 (Proxy) QCS8275 Proxy TFLITE 571.291 ms 8 - 18 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8275 (Proxy) QCS8275 Proxy QNN 281.893 ms 0 - 10 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8550 (Proxy) QCS8550 Proxy TFLITE 116.297 ms 0 - 38 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8550 (Proxy) QCS8550 Proxy QNN 23.278 ms 0 - 3 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS9075 (Proxy) QCS9075 Proxy TFLITE 410.407 ms 11 - 21 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS9075 (Proxy) QCS9075 Proxy QNN 31.266 ms 0 - 10 MB FP16 NPU Use Export Script
EasyOCRRecognizer QCS8450 (Proxy) QCS8450 Proxy TFLITE 164.69 ms 5 - 27 MB FP32 CPU EasyOCR.tflite
EasyOCRRecognizer QCS8450 (Proxy) QCS8450 Proxy QNN 36.284 ms 0 - 170 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon X Elite CRD Snapdragon® X Elite QNN 24.48 ms 0 - 0 MB FP16 NPU Use Export Script
EasyOCRRecognizer Snapdragon X Elite CRD Snapdragon® X Elite ONNX 17.61 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)   : 41.7                   
Estimated peak memory usage (MB): [1, 132]               
Total # Ops                     : 42                     
Compute Unit(s)                 : NPU (42 ops)           

------------------------------------------------------------
EasyOCRRecognizer
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 117.6                  
Estimated peak memory usage (MB): [3, 6]                 
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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