Comparison Between Huawei ML Kit Text Recognition & Firebase ML Kit Text Recognition - Huawei Developers

Comparison Between Huawei ML Kit Text Recognition & Firebase ML Kit Text Recognition
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
In this article, we will compare Huawei ML Kit Text Recognition and Firebase ML Kit Text Recognition usages and also we will create sample Android applications to understand how they work. Lets get start it.
Huawei ML Kit Text Recognition
About The Service
HUAWEI ML Kit allows your apps to easily leverage Huawei’s long-term proven expertise in machine learning to support diverse artificial intelligence (AI) applications throughout a wide range of industries. Thanks to Huawei’s technology accumulation, ML Kit provides diversified leading machine learning capabilities that are easy to use, helping you develop various AI apps.
Text Recognition
The text recognition service can extracts text from images of receipts, business cards, and documents. This service is widely used in office, education, transit, and other apps. For example, you can use this service in a translation app to extract text in a photo and translate the text, improving user experience.
This service can run on the cloud or device, but the supported languages differ in the two scenarios. On-device APIs can recognize text in Simplified Chinese, Japanese, Korean, and Latin-based languages (refer to Latin Script Supported by On-device Text Recognition). When running on the cloud, the service can recognize text in languages such as Simplified Chinese, English, Spanish, Portuguese, Italian, German, French, Russian, Japanese, Korean, Polish, Finnish, Norwegian, Swedish, Danish, Turkish, Thai, Arabic, Hindi, and Indonesian.
Configure your project on AppGallery Connect
Registering a Huawei ID
You need to register a Huawei ID to use the plugin. If you don’t have one, follow the instructions here.
Preparations for Integrating HUAWEI HMS Core
First of all, you need to integrate Huawei Mobile Services with your application. I will not get into details about how to integrate your application but you can use this tutorial as step by step guide.
1. Integrating the Text Recognition SDK
You need to integrate the base SDK and then one or more required language model packages in your app-level build.gradle.
Code:
//AGC Core
implementation 'com.huawei.agconnect:agconnect-core:1.4.0.300'
//ML OCR Base SDK
implementation 'com.huawei.hms:ml-computer-vision-ocr:2.0.1.300'
//Latin-based Language Model Package
implementation 'com.huawei.hms:ml-computer-vision-ocr-latin-model:2.0.1.300'
2. Automatically Updating the Machine Learning Model
To use the on-device text recognition service, add the following statements to the AndroidManifest.xml file.
Code:
<manifest
...
<meta-data
android:name="com.huawei.hms.ml.DEPENDENCY"
android:value= "ocr"/>
...
</manifest>
3. There will be an ImageView, a TextView and two Button in our RelativeLayout
Code:
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".MainActivity">
<ImageView
android:id="@+id/captured_image_view"
android:layout_width="match_parent"
android:layout_height="400dp" />
<TextView
android:id="@+id/detected_text_view"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_below="@+id/captured_image_view"
android:textSize="20sp"
android:maxLines="10"
android:layout_margin="10dp"
/>
<Button
android:id="@+id/capture_image"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_above="@+id/detect_text_from_image"
android:text="@string/button_text"
android:textAllCaps="false"
android:background="@color/colorAccent"
android:textColor="@android:color/white"
android:textSize="28sp"
android:layout_marginBottom="5dp"
/>
<Button
android:id="@+id/detect_text_from_image"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_alignParentBottom="true"
android:text="@string/button_detect"
android:textAllCaps="false"
android:background="@color/colorPrimary"
android:textColor="@android:color/white"
android:textSize="28sp"
/>
</RelativeLayout>
4. Text Recognition from Images on the Device
Take a photo with a camera app
The Android way of delegating actions to other applications is to invoke an Intent that describes what you want done. This process involves three pieces: The Intent itself, a call to start the external Activity, and some code to handle the image data when focus returns to your activity.We will see the result into onActivityResult.
Code:
import androidx.annotation.Nullable;
import androidx.appcompat.app.AppCompatActivity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.os.Bundle;
import android.provider.MediaStore;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import com.huawei.hmf.tasks.OnFailureListener;
import com.huawei.hmf.tasks.OnSuccessListener;
import com.huawei.hmf.tasks.Task;
import com.huawei.hms.mlsdk.MLAnalyzerFactory;
import com.huawei.hms.mlsdk.common.MLFrame;
import com.huawei.hms.mlsdk.text.MLLocalTextSetting;
import com.huawei.hms.mlsdk.text.MLText;
import com.huawei.hms.mlsdk.text.MLTextAnalyzer;
import java.io.IOException;
public class MainActivity extends AppCompatActivity {
static final int REQUEST_IMAGE_CAPTURE = 1;
private MLTextAnalyzer mTextAnalyzer;
private ImageView capturedImageView;
private TextView detectedTextView;
private Button buttonAddImage, detectTextBtn;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
capturedImageView = findViewById(R.id.captured_image_view);
detectedTextView = findViewById(R.id.detected_text_view);
buttonAddImage = findViewById(R.id.capture_image);
detectTextBtn=findViewById(R.id.detect_text_from_image);
buttonAddImage.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
dispatchTakePictureIntent();
detectedTextView.setText("");
}
});
detectTextBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
createMLTextAnalyzer();
}
});
}
private void dispatchTakePictureIntent() {
Intent intent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
if (intent.resolveActivity(getPackageManager()) != null) {
startActivityForResult(intent, REQUEST_IMAGE_CAPTURE);
}
}
@Override
protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (requestCode == REQUEST_IMAGE_CAPTURE && resultCode == RESULT_OK && data != null) {
Bundle extras = data.getExtras();
Bitmap selectedBitmap = (Bitmap) extras.get("data");
capturedImageView.setImageBitmap(selectedBitmap);
asyncAnalyzeText(selectedBitmap);
}
}
5. Create the text analyzer MLTextAnalyzer to recognize text in images. You can set MLLocalTextSetting to specify languages that can be recognized. If you do not set the languages, only Latin-based languages can be recognized by default.
Code:
private void createMLTextAnalyzer() {
MLLocalTextSetting setting = new MLLocalTextSetting.Factory()
.setOCRMode(MLLocalTextSetting.OCR_DETECT_MODE)
.setLanguage("en")
.create();
mTextAnalyzer = MLAnalyzerFactory.getInstance().getLocalTextAnalyzer(setting);
}
6. Pass the MLFrame object to the asyncAnalyseFrame method for text recognition.
Code:
private void asyncAnalyzeText(Bitmap bitmap) {
if (mTextAnalyzer == null) {
createMLTextAnalyzer();
}
MLFrame frame = MLFrame.fromBitmap(bitmap);
Task<MLText> task = mTextAnalyzer.asyncAnalyseFrame(frame);
task.addOnSuccessListener(new OnSuccessListener<MLText>() {
@Override
public void onSuccess(MLText text) {
detectedTextView.setText(text.getStringValue());
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(Exception e) {
detectedTextView.setText(e.getMessage());
}
});
}
7. After the recognition is complete, stop the analyzer to release recognition resources.
Code:
@Override
protected void onDestroy() {
super.onDestroy();
try {
if (mTextAnalyzer != null)
mTextAnalyzer.stop();
} catch (IOException e) {
e.printStackTrace();
}
}
8. You can see all the code in Main Activity below.
Code:
import androidx.annotation.Nullable;
import androidx.appcompat.app.AppCompatActivity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.os.Bundle;
import android.provider.MediaStore;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import com.huawei.hmf.tasks.OnFailureListener;
import com.huawei.hmf.tasks.OnSuccessListener;
import com.huawei.hmf.tasks.Task;
import com.huawei.hms.mlsdk.MLAnalyzerFactory;
import com.huawei.hms.mlsdk.common.MLFrame;
import com.huawei.hms.mlsdk.text.MLLocalTextSetting;
import com.huawei.hms.mlsdk.text.MLText;
import com.huawei.hms.mlsdk.text.MLTextAnalyzer;
import java.io.IOException;
public class MainActivity extends AppCompatActivity {
static final int REQUEST_IMAGE_CAPTURE = 1;
private MLTextAnalyzer mTextAnalyzer;
private ImageView capturedImageView;
private TextView detectedTextView;
private Button buttonAddImage, detectTextBtn;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
capturedImageView = findViewById(R.id.captured_image_view);
detectedTextView = findViewById(R.id.detected_text_view);
buttonAddImage = findViewById(R.id.capture_image);
detectTextBtn=findViewById(R.id.detect_text_from_image);
buttonAddImage.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
dispatchTakePictureIntent();
detectedTextView.setText("");
}
});
detectTextBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
createMLTextAnalyzer();
}
});
}
private void dispatchTakePictureIntent() {
Intent intent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
if (intent.resolveActivity(getPackageManager()) != null) {
startActivityForResult(intent, REQUEST_IMAGE_CAPTURE);
}
}
@Override
protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (requestCode == REQUEST_IMAGE_CAPTURE && resultCode == RESULT_OK && data != null) {
Bundle extras = data.getExtras();
Bitmap selectedBitmap = (Bitmap) extras.get("data");
capturedImageView.setImageBitmap(selectedBitmap);
asyncAnalyzeText(selectedBitmap);
}
}
private void createMLTextAnalyzer() {
MLLocalTextSetting setting = new MLLocalTextSetting.Factory()
.setOCRMode(MLLocalTextSetting.OCR_DETECT_MODE)
.setLanguage("en")
.create();
mTextAnalyzer = MLAnalyzerFactory.getInstance().getLocalTextAnalyzer(setting);
}
private void asyncAnalyzeText(Bitmap bitmap) {
if (mTextAnalyzer == null) {
createMLTextAnalyzer();
}
MLFrame frame = MLFrame.fromBitmap(bitmap);
Task<MLText> task = mTextAnalyzer.asyncAnalyseFrame(frame);
task.addOnSuccessListener(new OnSuccessListener<MLText>() {
@Override
public void onSuccess(MLText text) {
detectedTextView.setText(text.getStringValue());
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(Exception e) {
detectedTextView.setText(e.getMessage());
}
});
}
@Override
protected void onDestroy() {
super.onDestroy();
try {
if (mTextAnalyzer != null)
mTextAnalyzer.stop();
} catch (IOException e) {
e.printStackTrace();
}
}
}
9. Here’s the result.
Firebase ML Kit Text Recognition
You can use ML Kit to recognize text in images. ML Kit has both a general-purpose API suitable for recognizing text in images, such as the text of a street sign, and an API optimized for recognizing the text of documents. The general-purpose API has both on-device and cloud-based models.
Before you begin
1. If you haven’t already, add Firebase to your Android project.
2. In your project-level build.gradle file, make sure to include Google's Maven repository in both your buildscript and allprojects sections.
3. Add the dependencies for the ML Kit Android libraries to your module (app-level) Gradle file (usually app/build.gradle):
Code:
apply plugin: 'com.android.application'
apply plugin: 'com.google.gms.google-services'
dependencies {
// ...
implementation 'com.google.firebase:firebase-core:15.0.2'
implementation 'com.google.firebase:firebase-ml-vision:15.0.0'
}
4. add the following declaration to your app’s AndroidManifest.xml file
Code:
<uses-permission android:name="android.permission.INTERNET" />
<uses-feature android:name="android.hardware.camera"
android:required="true" />
<application ...>
...
<meta-data
android:name="com.google.firebase.ml.vision.DEPENDENCIES"
android:value="ocr" />
<!-- To use multiple models: android:value="ocr,model2,model3" -->
</application>
5. There will be an ImageView, a TextView and two Button in our RelativeLayout
Code:
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".MainActivity">
<ImageView
android:id="@+id/captured_image_view"
android:layout_width="match_parent"
android:layout_height="400dp" />
<TextView
android:id="@+id/detected_text_view"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_below="@+id/captured_image_view"
android:textSize="20sp"
android:maxLines="10"
android:layout_margin="10dp"
/>
<Button
android:id="@+id/capture_image"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_above="@+id/detect_text_from_image"
android:text="@string/button_text"
android:textAllCaps="false"
android:background="@color/colorAccent"
android:textColor="@android:color/white"
android:textSize="28sp"
android:layout_marginBottom="5dp"
/>
<Button
android:id="@+id/detect_text_from_image"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_alignParentBottom="true"
android:text="@string/button_detect"
android:textAllCaps="false"
android:background="@color/colorPrimary"
android:textColor="@android:color/white"
android:textSize="28sp"
/>
</RelativeLayout>
6. Take a photo with a camera app
Here’s a function that invokes an intent to capture a photo.
Code:
import com.google.android.gms.tasks.OnFailureListener;
import com.google.android.gms.tasks.OnSuccessListener;
import com.google.firebase.ml.vision.FirebaseVision;
import com.google.firebase.ml.vision.common.FirebaseVisionImage;
import com.google.firebase.ml.vision.text.FirebaseVisionText;
import com.google.firebase.ml.vision.text.FirebaseVisionTextDetector;
import java.util.List;
public class MainActivity extends AppCompatActivity {
private Button captureImageBtn, detectTextBtn;
private ImageView capturedImageView;
private TextView detectedTextView;
static final int REQUEST_IMAGE_CAPTURE = 1;
Bitmap imageBitmap;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
captureImageBtn = findViewById(R.id.capture_image);
detectTextBtn = findViewById(R.id.detect_text_from_image);
capturedImageView = findViewById(R.id.captured_image_view);
detectedTextView = findViewById(R.id.detected_text_view);
captureImageBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
dispatchTakePictureIntent();
detectedTextView.setText("");
}
});
detectTextBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
detectTextFromImage();
}
});
}
private void dispatchTakePictureIntent() {
Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
startActivityForResult(takePictureIntent, REQUEST_IMAGE_CAPTURE);
}
}
7. The Android Camera application encodes the photo in the return Intent delivered to onActivityResult() as a small Bitmap in the extras, under the key "data". The following code retrieves this image and displays it in an ImageView.
Code:
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (requestCode == REQUEST_IMAGE_CAPTURE && resultCode == RESULT_OK) {
Bundle extras = data.getExtras();
imageBitmap = (Bitmap) extras.get("data");
capturedImageView.setImageBitmap(imageBitmap);
}
}
8. To create a FirebaseVisionImage object from a Bitmap object.
Code:
private void detectTextFromImage() {
FirebaseVisionImage firebaseVisionImage = FirebaseVisionImage.fromBitmap(imageBitmap);
FirebaseVisionTextDetector firebaseVisionTextDetector = FirebaseVision.getInstance().getVisionTextDetector();
firebaseVisionTextDetector.detectInImage(firebaseVisionImage).addOnSuccessListener(new OnSuccessListener<FirebaseVisionText>() {
@Override
public void onSuccess(FirebaseVisionText firebaseVisionText) {
displayTextFromImage(firebaseVisionText);
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
Toast.makeText(MainActivity.this, "Error: " + e.getMessage(), Toast.LENGTH_SHORT).show();
Log.d("Error", e.getMessage());
}
});
}
9. To display text from the image
Code:
private void displayTextFromImage(FirebaseVisionText firebaseVisionText) {
List<FirebaseVisionText.Block>blockList=firebaseVisionText.getBlocks();
if(blockList.size()==0){
Toast.makeText(this, "No Text Found in Image", Toast.LENGTH_SHORT).show();
}else{
for(FirebaseVisionText.Block block: firebaseVisionText.getBlocks()){
String text=block.getText();
detectedTextView.setText(text);
}
}
}
10. You can see all the code in Main Activity below.
Code:
import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import android.content.Intent;
import android.graphics.Bitmap;
import android.os.Bundle;
import android.provider.MediaStore;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import com.google.android.gms.tasks.OnFailureListener;
import com.google.android.gms.tasks.OnSuccessListener;
import com.google.firebase.ml.vision.FirebaseVision;
import com.google.firebase.ml.vision.common.FirebaseVisionImage;
import com.google.firebase.ml.vision.text.FirebaseVisionText;
import com.google.firebase.ml.vision.text.FirebaseVisionTextDetector;
import java.util.List;
public class MainActivity extends AppCompatActivity {
private Button captureImageBtn, detectTextBtn;
private ImageView capturedImageView;
private TextView detectedTextView;
static final int REQUEST_IMAGE_CAPTURE = 1;
Bitmap imageBitmap;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
captureImageBtn = findViewById(R.id.capture_image);
detectTextBtn = findViewById(R.id.detect_text_from_image);
capturedImageView = findViewById(R.id.captured_image_view);
detectedTextView = findViewById(R.id.detected_text_view);
captureImageBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
dispatchTakePictureIntent();
detectedTextView.setText("");
}
});
detectTextBtn.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
detectTextFromImage();
}
});
}
private void dispatchTakePictureIntent() {
Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
startActivityForResult(takePictureIntent, REQUEST_IMAGE_CAPTURE);
}
}
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (requestCode == REQUEST_IMAGE_CAPTURE && resultCode == RESULT_OK) {
Bundle extras = data.getExtras();
imageBitmap = (Bitmap) extras.get("data");
capturedImageView.setImageBitmap(imageBitmap);
}
}
private void detectTextFromImage() {
FirebaseVisionImage firebaseVisionImage = FirebaseVisionImage.fromBitmap(imageBitmap);
FirebaseVisionTextDetector firebaseVisionTextDetector = FirebaseVision.getInstance().getVisionTextDetector();
firebaseVisionTextDetector.detectInImage(firebaseVisionImage).addOnSuccessListener(new OnSuccessListener<FirebaseVisionText>() {
@Override
public void onSuccess(FirebaseVisionText firebaseVisionText) {
displayTextFromImage(firebaseVisionText);
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
Toast.makeText(MainActivity.this, "Error: " + e.getMessage(), Toast.LENGTH_SHORT).show();
Log.d("Error", e.getMessage());
}
});
}
private void displayTextFromImage(FirebaseVisionText firebaseVisionText) {
List<FirebaseVisionText.Block>blockList=firebaseVisionText.getBlocks();
if(blockList.size()==0){
Toast.makeText(this, "No Text Found in Image", Toast.LENGTH_SHORT).show();
}else{
for(FirebaseVisionText.Block block: firebaseVisionText.getBlocks()){
String text=block.getText();
detectedTextView.setText(text);
}
}
}
}
11. Here’s the result.
For more information about HUAWEI ML Kit, visit:
https://developer.huawei.com/consumer/en/hms/huawei-mlkit
Other Resources:
https://developer.android.com/training/camera/photobasics#java
https://firebase.google.com/docs/ml-kit/android/recognize-text
https://firebase.google.com/support/release-notes/android
Related Links
Original post: https://medium.com/huawei-developers/comparison-between-huawei-ml-kit-text-recognition-and-firebase-ml-kit-text-recognition-98217e3cfa84

---

Nice article
Is huawei ML kit is better than firebase ML kit?

---

riteshchanchal said:
Is huawei ML kit is better than firebase ML kit?
Click to expand...
Click to collapse
I'm not sure which one is better here I just used text recognition. But in this example the Huawei Ml kit worked better. Also, payment is required to use the latest version of the Firebase ML kit but Huawei ML kit is free.