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: Model_use_case.image_to_text
• Model Stats:
  • Model checkpoint: easyocr-small-stage1
  • Input resolution: 608x800
  • Number of parameters (EasyOCRDetector): 20.8M
  • Model size (EasyOCRDetector) (float): 79.2 MB
  • Number of parameters (EasyOCRRecognizer): 3.84M
  • Model size (EasyOCRRecognizer) (float): 14.7 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
EasyOCRDetector	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	272.724 ms	1 - 37 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	67.771 ms	1 - 50 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	80.455 ms	6 - 58 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	38.25 ms	0 - 149 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	39.118 ms	6 - 21 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	37.288 ms	0 - 100 MB	NPU	EasyOCR.onnx.zip
EasyOCRDetector	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	70.019 ms	1 - 36 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	272.724 ms	1 - 37 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	38.555 ms	0 - 154 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	39.524 ms	6 - 19 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	76.766 ms	1 - 41 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	77.364 ms	4 - 53 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	38.793 ms	1 - 158 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	39.455 ms	6 - 17 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	70.019 ms	1 - 36 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	28.252 ms	0 - 49 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	28.684 ms	6 - 47 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	28.055 ms	6 - 46 MB	NPU	EasyOCR.onnx.zip
EasyOCRDetector	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	22.89 ms	1 - 42 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	22.785 ms	6 - 44 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	22.177 ms	2 - 41 MB	NPU	EasyOCR.onnx.zip
EasyOCRDetector	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	17.023 ms	1 - 42 MB	NPU	EasyOCR.tflite
EasyOCRDetector	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	19.475 ms	6 - 45 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	16.737 ms	7 - 49 MB	NPU	EasyOCR.onnx.zip
EasyOCRDetector	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	40.696 ms	12 - 12 MB	NPU	EasyOCR.dlc
EasyOCRDetector	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	38.568 ms	35 - 35 MB	NPU	EasyOCR.onnx.zip
EasyOCRRecognizer	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	546.028 ms	8 - 18 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	126.589 ms	9 - 32 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	38.074 ms	0 - 199 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	107.353 ms	8 - 10 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	24.875 ms	0 - 122 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	35.27 ms	0 - 77 MB	NPU	EasyOCR.onnx.zip
EasyOCRRecognizer	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	378.609 ms	10 - 21 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	546.028 ms	8 - 18 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	101.803 ms	8 - 10 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	24.83 ms	0 - 129 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	223.277 ms	11 - 29 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	40.68 ms	0 - 195 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	100.96 ms	8 - 10 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	24.749 ms	0 - 120 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	378.609 ms	10 - 21 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	96.781 ms	9 - 29 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	18.626 ms	0 - 462 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	26.523 ms	0 - 273 MB	NPU	EasyOCR.onnx.zip
EasyOCRRecognizer	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	116.293 ms	11 - 26 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	16.329 ms	0 - 473 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	21.433 ms	0 - 278 MB	NPU	EasyOCR.onnx.zip
EasyOCRRecognizer	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	74.689 ms	14 - 25 MB	CPU	EasyOCR.tflite
EasyOCRRecognizer	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	17.489 ms	0 - 509 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	19.727 ms	0 - 304 MB	NPU	EasyOCR.onnx.zip
EasyOCRRecognizer	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	24.652 ms	100 - 100 MB	NPU	EasyOCR.dlc
EasyOCRRecognizer	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	36.052 ms	12 - 12 MB	NPU	EasyOCR.onnx.zip

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
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

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
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# 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 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

• 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.