README.md

---
library_name: pytorch
license: apache-2.0
tags:
- android
pipeline_tag: image-to-text

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/easyocr/web-assets/model_demo.png)

# 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](https://github.com/JaidedAI/EasyOCR).


This repository provides scripts to run EasyOCR on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/easyocr).


### 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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) |
| EasyOCRDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 37.7 ms | 6 - 17 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.so) |
| EasyOCRDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 41.887 ms | 32 - 121 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) |
| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 30.07 ms | 14 - 72 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) |
| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 28.085 ms | 6 - 24 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.so) |
| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 30.704 ms | 42 - 74 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) |
| EasyOCRDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 29.358 ms | 14 - 48 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) |
| EasyOCRDetector | SA7255P ADP | SA7255P | TFLITE | 2113.984 ms | 0 - 30 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) |
| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 117.587 ms | 3 - 6 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) |
| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 23.252 ms | 0 - 3 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.so) |
| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 20.753 ms | 0 - 22 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) |
| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 109.883 ms | 9 - 29 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) |
| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16.886 ms | 0 - 18 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.so) |
| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 16.816 ms | 0 - 25 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) |
| EasyOCRRecognizer | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 106.439 ms | 20 - 35 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) |
| EasyOCRRecognizer | SA7255P ADP | SA7255P | TFLITE | 571.291 ms | 8 - 18 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.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](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) |




## Installation


Install the package via pip:
```bash
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](https://app.aihub.qualcomm.com/) 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.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/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.

```bash
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.

```bash
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](https://aihub.qualcomm.com/models/easyocr/qai_hub_models/models/EasyOCR/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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.

```python
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.
```python
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.
```python
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](https://myaccount.qualcomm.com/signup).




## Deploying compiled model to Android


The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
  guide to deploy the .tflite model in an Android application.


- QNN (`.so` export ): This [sample
  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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](https://aihub.qualcomm.com/models/easyocr).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


## License
* The license for the original implementation of EasyOCR can be found
  [here](https://github.com/JaidedAI/EasyOCR/blob/master/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



## References
* [Source Model Implementation](https://github.com/JaidedAI/EasyOCR)



## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).