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Demystifying Document Skew Correction feat. HUAWEI ML KIT

Demystifying Document Skew Correction feat. HUAWEI ML KIT
Prolusion
https://preview.redd.it/nk3xz1sjqrw51.png?width=547&format=png&auto=webp&s=f4db4c0691af78cc92770fc4c9510751eeff5eb2
This era is revolutionary for the science and research as most of the innovation is for the consumer needs.
We all know that document scanning is routine errand for most of us and a dire need for today’s digital world.
In such needs, we often require a powerful mechanism which can correct the informalities and skew for our documents.
Document Skew Correction is a technique which helps in correcting the tilted images to the right facing angle which further improvise the visibility of the image.
Huawei ML Kit offers a robust API for skew correction which enables automatic position identification of a document in an image and corrects the shooting angle. It also allows users to customize the edge points.
https://preview.redd.it/pkjm73lkqrw51.png?width=513&format=png&auto=webp&s=fd49d1d4f40364092c54eb35acd7057fe41d55fa
Suggestions
  1. It is recommended the shooting angle of the image should be within 30 degrees.
  2. It is recommended that the image size be within the range of 320 x 320 px to 1920 x 1920 px.
  3. Skew detection API supports JPG, JPEG, and PNG image formats.
Development Overview
Prerequisite
  1. Must have a Huawei Developer Account
  2. Must have Android Studio 3.0 or later
  3. Must have a Huawei phone with HMS Core 4.0.2.300 or later
  4. EMUI 3.0 or later
Software Requirements
  1. Java SDK 1.7 or later
  2. Android 5.0 or later
Preparation
  1. Create an app or project in the Huawei app gallery connect.
  2. Provide the SHA Key and App Package name of the project in App Information Section and enable the ML Kit API.
  3. Download the agconnect-services.json file.
  4. Create an Android project.
Integration
Add below to build.gradle (project)file, under buildscript/repositories and allprojects/repositories.
Maven {url 'http://developer.huawei.com/repo/'} 
Add below to build.gradle (app) file, under dependencies.
To use the Base SDK of ML Kit-Document Skew Correction, add the following dependencies:
dependencies{ 
// Import the base SDK. implementation 'com.huawei.hms:ml-computer-vision-documentskew:2.0.4.300' }
To use the Full SDK of ML Kit- Document Skew Correction, add the following dependencies:
dependencies{ 
// Import the Model Package. implementation 'com.huawei.hms:ml-computer-vision-documentskew-model:2.0.4.300' }
Adding permissions
 

Automatically Updating the Machine Learning Model
Add the following statements to the AndroidManifest.xml file to automatically install the machine learning model on the user’s device.
 android:name="com.huawei.hms.ml.DEPENDENCY" android:value= "dsc"/>
Development Process
This article focuses on demonstrating the capabilities of Huawei’s ML Kit: Document Skew Correction API’s.
Here is the example of “SUPER DOC” application which allows user to capture and fetch the images from local memory of the device and let them correct using the document which explains how we can integrate this powerful API to leverage the benefits of correcting a skewed document image to provider the right angle to the document which eventually improves the readability of the document.
Skewdetect Activity
This activity is responsible to click and fetch the images and detect them for the skew correction and further align them and provide the output as aligned document image.
package com.mlkit.documentSkewCorrection; import java.io.IOException; import java.util.ArrayList; import java.util.List; import android.content.Intent; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android.graphics.Point; import android.net.Uri; import android.os.Bundle; import android.provider.MediaStore; import android.util.Log; import android.view.View; import android.widget.ImageView; import android.widget.Toast; import androidx.appcompat.app.AppCompatActivity; import com.google.android.material.floatingactionbutton.FloatingActionButton; import com.huawei.hmf.tasks.OnFailureListener; import com.huawei.hmf.tasks.OnSuccessListener; import com.huawei.hmf.tasks.Task; import com.huawei.hms.mlsdk.common.MLFrame; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionConstant; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionCoordinateInput; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionResult; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionAnalyzer; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionAnalyzerFactory; import com.huawei.hms.mlsdk.dsc.MLDocumentSkewCorrectionAnalyzerSetting; import ccom.huawei.hms.mlsdk.dsc.MLDocumentSkewDetectResult; import com.mlkit.documentSkewCorrection.R; public class SkewDetect extends AppCompatActivity implements View.OnClickListener { private static final String TAG = "SkewDetectActivity"; private MLDocumentSkewCorrectionAnalyzer analyzer; private ImageView mImageView; private Bitmap bitmap; Uri imageUri; private MLDocumentSkewCorrectionCoordinateInput input; private MLFrame mlFrame; Boolean FlagCameraClickDone = false; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); this.setContentView(R.layout.activity_document_skew_correction); this.findViewById(R.id.image_refine).setOnClickListener(this); this.mImageView = this.findViewById(R.id.image_refine_result); if(FlagCameraClickDone) { this.findViewById(R.id.image_refine).setVisibility(View.VISIBLE); } else { this.findViewById(R.id.image_refine).setVisibility(View.GONE); } // Create the setting. MLDocumentSkewCorrectionAnalyzerSetting setting = new MLDocumentSkewCorrectionAnalyzerSetting .Factory() .create(); // Get the analyzer. this.analyzer = MLDocumentSkewCorrectionAnalyzerFactory.getInstance().getDocumentSkewCorrectionAnalyzer(setting); FloatingActionButton fab = (FloatingActionButton) findViewById(R.id.fab); fab.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View view) { FlagCameraClickDone = false; Intent gallery = new Intent(Intent.ACTION_PICK, MediaStore.Images.Media.EXTERNAL_CONTENT_URI); startActivityForResult(gallery, 1); } }); } @Override protected void onActivityResult(int requestCode, int resultCode, Intent data){ super.onActivityResult(requestCode, resultCode, data); if (resultCode == RESULT_OK && requestCode == 1){ imageUri = data.getData(); try { bitmap = MediaStore.Images.Media.getBitmap(this.getContentResolver(), imageUri); // Create a MLFrame by using the bitmap. this.mlFrame = new MLFrame.Creator().setBitmap(this.bitmap).create(); } catch (IOException e) { e.printStackTrace(); } //BitmapFactory.decodeResource(getResources(), R.drawable.new1); FlagCameraClickDone = true; this.findViewById(R.id.image_refine).setVisibility(View.VISIBLE); mImageView.setImageURI(imageUri); } } @Override public void onClick(View v) { this.analyzer(); } private void analyzer() { // Call document skew detect interface to get coordinate data Task detectTask = this.analyzer.asyncDocumentSkewDetect(this.mlFrame); detectTask.addOnSuccessListener(new OnSuccessListener() { @Override public void onSuccess(MLDocumentSkewDetectResult detectResult) { Log.e(TAG, detectResult.getResultCode() + ":"); if (detectResult != null) { int resultCode = detectResult.getResultCode(); // Detect success. if (resultCode == MLDocumentSkewCorrectionConstant.SUCCESS) { Point leftTop = detectResult.getLeftTopPosition(); Point rightTop = detectResult.getRightTopPosition(); Point leftBottom = detectResult.getLeftBottomPosition(); Point rightBottom = detectResult.getRightBottomPosition(); List coordinates = new ArrayList<>(); coordinates.add(leftTop); coordinates.add(rightTop); coordinates.add(rightBottom); coordinates.add(leftBottom); SkewDetect .this.setDetectData(new MLDocumentSkewCorrectionCoordinateInput(coordinates)); SkewDetect .this.refineImg(); } else if (resultCode == MLDocumentSkewCorrectionConstant.IMAGE_DATA_ERROR) { // Parameters error. Log.e(TAG, "Parameters error!"); SkewDetect.this.displayFailure(); } else if (resultCode == MLDocumentSkewCorrectionConstant.DETECT_FAILD) { // Detect failure. Log.e(TAG, "Detect failed!"); SkewDetect .this.displayFailure(); } } else { // Detect exception. Log.e(TAG, "Detect exception!"); SkewDetect .this.displayFailure(); } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(Exception e) { // Processing logic for detect failure. Log.e(TAG, e.getMessage() + ""); SkewDetect .this.displayFailure(); } }); } // Show result private void displaySuccess(MLDocumentSkewCorrectionResult refineResult) { if (this.bitmap == null) { this.displayFailure(); return; } // Draw the portrait with a transparent background. Bitmap corrected = refineResult.getCorrected(); if (corrected != null) { this.mImageView.setImageBitmap(corrected); } else { this.displayFailure(); } } private void displayFailure() { Toast.makeText(this.getApplicationContext(), "Fail", Toast.LENGTH_SHORT).show(); } private void setDetectData(MLDocumentSkewCorrectionCoordinateInput input) { this.input = input; } // Refine image private void refineImg() { // Call refine image interface Task correctionTask = this.analyzer.asyncDocumentSkewCorrect(this.mlFrame, this.input); correctionTask.addOnSuccessListener(new OnSuccessListener() { @Override public void onSuccess(MLDocumentSkewCorrectionResult refineResult) { if (refineResult != null) { int resultCode = refineResult.getResultCode(); if (resultCode == MLDocumentSkewCorrectionConstant.SUCCESS) { SkewDetect .this.displaySuccess(refineResult); } else if (resultCode == MLDocumentSkewCorrectionConstant.IMAGE_DATA_ERROR) { // Parameters error. Log.e(TAG, "Parameters error!"); SkewDetect .this.displayFailure(); } else if (resultCode == MLDocumentSkewCorrectionConstant.CORRECTION_FAILD) { // Correct failure. Log.e(TAG, "Correct failed!"); SkewDetect .this.displayFailure(); } } else { // Correct exception. Log.e(TAG, "Correct exception!"); SkewDetect .this.displayFailure(); } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(Exception e) { // Processing logic for refine failure. SkewDetect .this.displayFailure(); } }); } @Override protected void onDestroy() { super.onDestroy(); if (this.analyzer != null) { try { this.analyzer.stop(); } catch (IOException e) { Log.e(SkewDetect .TAG, "Stop failed: " + e.getMessage()); } } } } 
Skewdetect Activity View Class
This class is responsible for creating the UI definition of the application.
  /**Create an image view to hold the bitmap**/   /**Create a button to fetch the ML kit API for skew correction**/  /**Create a button to capture the image for skew correction**/  /**Create a button to fetch the image for skew correction**/    
Results
https://preview.redd.it/8lv5fotwqrw51.png?width=188&format=png&auto=webp&s=9d02baa94f5453e4ce286bb1b04913a916a06f49
https://preview.redd.it/ikcwsr5xqrw51.png?width=188&format=png&auto=webp&s=c5219e15993a26faf66f6a4ae9ad66e0f974f278
Conclusion
In this article we took a small step to create and demonstrate the integration of Document Skew Correction API’s from Huawei ML Kit for better document image readability.
Upcoming article will have the integration of multiple ML API’s under one powerful application.
Stay tuned!!
References
https://developer.huawei.com/consumeen/doc/development/HMSCore-Guides/documentskewcorrection-0000001051703156
submitted by helloworddd to HuaweiDevelopers

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