BlinkID SDK for ID scanning

BlinkID SDK is a delightful component for quick and easy scanning of ID cards, passports and drivers licenses. The SDK is powered with MicroBlink's industry-proven and world leading OCR and barcode scanning technology, and offers:

integrated camera management
layered API, allowing everything from simple integration to complex UX customizations.
lightweight and no internet connection required
enteprise-level security standards
data parsing from ID barcode standards

BlinkID is a part of family of SDKs developed by MicroBlink for optical text recognition, barcode scanning, ID document scanning and many others.

Requirements
Quick Start
Advanced BlinkInput integration instructions
MBRecognizer and available recognizers
Field by field scanning feature
- Field by field feature
MBProcessor and MBParser
Scanning generic documents with Templating API * The MBProcessorGroup component * List of available dewarp policies * The MBTemplatingClass component * Implementing the MBTemplatingClassifier
The MBDetector concept
- List of available detectors
  - Document Detector
  - MRTD Detector
Creating customized build of BlinkID SDK
Troubleshooting
Additional info

Requirements

SDK package contains Microblink framework and one or more sample apps which demonstrate framework integration. The framework can be deployed in iOS 8.0 or later, iPhone 4S or newer and iPad 2 or newer.

SDK performs significantly better when the images obtained from the camera are focused. Because of that, the SDK can have lower performance on iPad 2 and iPod Touch 4th gen devices, which don't have camera with autofocus.

Quick Start

Getting started with BlinkID SDK

This Quick Start guide will get you up and performing OCR scanning as quickly as possible. All steps described in this guide are required for the integration.

This guide sets up basic Raw OCR parsing and price parsing at the same time. It closely follows the BlinkOCR-sample app. We highly recommend you try to run the sample app. The sample app should compile and run on your device, and in the iOS Simulator.

The source code of the sample app can be used as the reference during the integration.

1. Initial integration steps

Using CocoaPods

If you wish to use version v1.2.0 or above, you need to install Git Large File Storage by running these comamnds:

brew install git-lfs
git lfs install

Be sure to restart your console after installing Git LFS
Project dependencies to be managed by CocoaPods are specified in a file called Podfile. Create this file in the same directory as your Xcode project (.xcodeproj) file.
Copy and paste the following lines into the TextEdit window:

platform :ios, '9.0'
pod 'PPBlinkID', '~> 4.3.0'

Install the dependencies in your project:

$ pod install

From now on, be sure to always open the generated Xcode workspace (.xcworkspace) instead of the project file when building your project:

open <YourProjectName>.xcworkspace

Integration without CocoaPods

-Download latest release (Download .zip or .tar.gz file starting with BlinkID. DO NOT download Source Code as GitHub does not fully support Git LFS)

Clone this git repository:

If you wish to clone version v1.4.0 or above, you need to install Git Large File Storage by running these comamnds:

brew install git-lfs
git lfs install

Be sure to restart your console after installing Git LFS
To clone, run the following shell command:

git clone git@github.com:BlinkID/blinkid-ios.git

Copy MicroBlink.framework and MicroBlink.bundle to your project folder.
In your Xcode project, open the Project navigator. Drag the MicroBlink.framework and MicroBlink.bundle files to your project, ideally in the Frameworks group, together with other frameworks you're using. When asked, choose "Create groups", instead of the "Create folder references" option.

Since Microblink.framework is a dynamic framework, you also need to add it to embedded binaries section in General settings of your target.

Include the additional frameworks and libraries into your project in the "Linked frameworks and libraries" section of your target settings.
- AudioToolbox.framework
- AVFoundation.framework
- CoreMedia.framework
- libc++.tbd
- libiconv.tbd
- libz.tbd

2. Referencing header file

In files in which you want to use scanning functionality place import directive.

Swift

import MicroBlink

Objective-C

#import <MicroBlink/MicroBlink.h>

3. Initiating the scanning process

To initiate the scanning process, first decide where in your app you want to add scanning functionality. Usually, users of the scanning library have a button which, when tapped, starts the scanning process. Initialization code is then placed in touch handler for that button. Here we're listing the initialization code as it looks in a touch handler method.

Also, for initialization purposes, the ViewController which initiates the scan have private prperties for MBRawParser, MBParserGroupProcessor and MBBlinkInputRecognizer, so we know how to obtain result.

class ViewController: UIViewController, MBDocumentOverlayViewControllerDelegate  {
    
    var rawParser: MBRawParser?
    var parserGroupProcessor: MBParserGroupProcessor?
    var blinkInputRecognizer: MBBlinkInputRecognizer?

    override func viewDidLoad() {
        super.viewDidLoad()
    }

    @IBAction func didTapScan(_ sender: AnyObject) {
        
        let settings = MBDocumentOverlaySettings()
        rawParser = MBRawParser()
        parserGroupProcessor = MBParserGroupProcessor(parsers: [rawParser!])
        blinkInputRecognizer = MBBlinkInputRecognizer(processors: [parserGroupProcessor!])
        
        let recognizerList = [self.blinkInputRecognizer!]
        let recognizerCollection : MBRecognizerCollection = MBRecognizerCollection(recognizers: recognizerList)
        
        /** Create your overlay view controller */
        let documentOverlayViewController : MBDocumentOverlayViewController = MBDocumentOverlayViewController(settings: settings, recognizerCollection: recognizerCollection, delegate: self)
        
        /** Create recognizer view controller with wanted overlay view controller */
        let recognizerRunneViewController : UIViewController = MBViewControllerFactory.recognizerRunnerViewController(withOverlayViewController: documentOverlayViewController)
        
        /** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
        present(recognizerRunnerViewController!, animated: true, completion: nil)
    }
}

@interface ViewController () <MBDocumentOverlayViewControllerDelegate>

@property (nonatomic, strong) MBRawParser *rawParser;
@property (nonatomic, strong) MBParserGroupProcessor *parserGroupProcessor;
@property (nonatomic, strong) MBBlinkInputRecognizer *blinkInputRecognizer;

@end

@implementation ViewController

- (void)viewDidLoad {
    [super viewDidLoad];
}


- (IBAction)didTapScan:(id)sender {
    
    MBDocumentOverlaySettings* settings = [[MBDocumentOverlaySettings alloc] init];

    self.rawParser = [[MBRawParser alloc] init];
    self.parserGroupProcessor = [[MBParserGroupProcessor alloc] initWithParsers:@[self.rawParser]];
    self.blinkInputRecognizer = [[MBBlinkInputRecognizer alloc] initWithProcessors:@[self.parserGroupProcessor]];

    /** Create recognizer collection */
    MBRecognizerCollection *recognizerCollection = [[MBRecognizerCollection alloc] initWithRecognizers:@[self.blinkInputRecognizer]];
    
    MBDocumentOverlayViewController *overlayVC = [[MBDocumentOverlayViewController alloc] initWithSettings:settings recognizerCollection:recognizerCollection delegate:self];
    UIViewController<MBRecognizerRunnerViewController>* recognizerRunnerViewController = [MBViewControllerFactory recognizerRunnerViewControllerWithOverlayViewController:overlayVC];
    
    /** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
    [self presentViewController:recognizerRunnerViewController animated:YES completion:nil];

}

@end

4. Registering for scanning events

In the previous step, you instantiated MBDocumentOverlayViewController object with a delegate object. This object gets notified on certain events in scanning lifecycle. In this example we set it to self. The protocol which the delegate has to implement is MBDocumentOverlayViewControllerDelegate protocol. It is necessary to conform to that protocol. We will discuss more about protocols in Advanced integration section. You can use the following default implementation of the protocol to get you started.

func documentOverlayViewControllerDidFinishScanning(_ documentOverlayViewController: MBDocumentOverlayViewController, state: MBRecognizerResultState) {

    // this is done on background thread
    // check for valid state
    if state == MBRecognizerResultState.valid {

        // first, pause scanning until we process all the results
        documentOverlayViewController.recognizerRunnerViewController?.pauseScanning()

        DispatchQueue.main.async(execute: {() -> Void in
            // All UI interaction needs to be done on main thread
        })
    }
}

func documentOverlayViewControllerDidTapClose(_ documentOverlayViewController: MBDocumentOverlayViewController) {
    // Your action on cancel 
}

- (void)documentOverlayViewControllerDidFinishScanning:(MBDocumentOverlayViewController *)documentOverlayViewController state:(MBRecognizerResultState)state {
    
    // this is done on background thread
    // check for valid state
    if (state == MBRecognizerResultStateValid) {
        
        // first, pause scanning until we process all the results
        [documentOverlayViewController.recognizerRunnerViewController pauseScanning];
        
        dispatch_async(dispatch_get_main_queue(), ^{
            // All UI interaction needs to be done on main thread
        });
    }
}

- (void)documentOverlayViewControllerDidTapClose:(MBDocumentOverlayViewController *)documentOverlayViewController {
    // Your action on cancel 
}

Advanced BlinkInput integration instructions

This section covers more advanced details of BlinkInput integration.

First part will cover the possible customizations when using UI provided by the SDK.
Second part will describe how to embed MBRecognizerRunnerViewController's delegates into your UIViewController with the goal of creating a custom UI for scanning, while still using camera management capabilites of the SDK.
Third part will describe how to use the MBRecognizerRunner (Direct API) for recognition directly from UIImage without the need of camera or to recognize camera frames that are obtained by custom camera management.
Fourth part will describe recognizer concept and available recognizers.

UI customizations of built-in `MBOverlayViewControllers` and `MBOverlaySubviews`

Built-in overlay view controllers and overlay subviews

Within BlinkID SDK there are several built-in overlay view controllers and scanning subview overlays that you can use to perform scanning.

`MBBarcodeOverlayViewController`

MBBarcodeOverlayViewController is overlay view controller best suited for performing scanning of various barcodes. It has MBBarcodeOverlayViewControllerDelegate delegate which can be used out of the box to perform scanning using the default UI.

`MBDocumentOverlayViewController`

MBBarcodeOverlayViewController is overlay view controller best suited for performing scanning of various document cards. It has MBDocumentOverlayViewControllerDelegate delegate which can be used out of the box to perform scanning using the default UI.

`MBDocumentVerificationOverlayViewController`

MBBarcodeOverlayViewController is overlay view controller best suited for performing scanning of various document for both front and back side. It has MBDocumentVerificationOverlayViewControllerDelegate delegate which can be used out of the box to perform scanning using the default UI.

Using `MBBarcodeOverlayViewController`

MBBarcodeOverlayViewController is built-in overlay view controller which is best suited to use while scanning various barcodes. Here is an example how to use and initialize MBBarcodeOverlayViewController:

Swift

/** Create your overlay view controller */
let barcodeOverlayViewController : MBBarcodeOverlayViewController = MBBarcodeOverlayViewController(settings: barcodeSettings, recognizerCollection: recognizerCollection, delegate: self)

/** Create recognizer view controller with wanted overlay view controller */
let recognizerRunneViewController : UIViewController = MBViewControllerFactory.recognizerRunnerViewController(withOverlayViewController: barcodeOverlayViewController)

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
self.present(recognizerRunneViewController, animated: true, completion: nil)

Objective-C

MBBarcodeOverlayViewController *overlayVC = [[MBBarcodeOverlayViewController alloc] initWithSettings:settings recognizerCollection: recognizerCollection delegate:self];
UIViewController<MBRecognizerRunnerViewController>* recognizerRunnerViewController = [MBViewControllerFactory recognizerRunnerViewControllerWithOverlayViewController:overlayVC];

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
[self presentViewController:recognizerRunnerViewController animated:YES completion:nil];

As you can see, when initializing MBBarcodeOverlayViewController, we are sending delegate property as self. To get results, we need to conform to MBBarcodeOverlayViewControllerDelegate protocol.

Using `MBDocumentOverlayViewController`

MBDocumentOverlayViewController is built-in overlay view controller which is best suited to use while scanning one side of a document. As you have seen in Quick Start, MBDocumentOverlayViewController has MBDocumentOverlaySettings. Here is an example how to use and initialize MBDocumentOverlayViewController:

Swift

/** Create your overlay view controller */
let documentOverlayViewController : MBDocumentOverlayViewController = MBDocumentOverlayViewController(settings: documentSettings, recognizerCollection: recognizerCollection, delegate: self)

/** Create recognizer view controller with wanted overlay view controller */
let recognizerRunneViewController : UIViewController = MBViewControllerFactory.recognizerRunnerViewController(withOverlayViewController: documentOverlayViewController)

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
self.present(recognizerRunneViewController, animated: true, completion: nil)

Objective-C

MBDocumentOverlayViewController *overlayVC = [[MBDocumentOverlayViewController alloc] initWithSettings:settings recognizerCollection: recognizerCollection delegate:self];
UIViewController<MBRecognizerRunnerViewController>* recognizerRunnerViewController = [MBViewControllerFactory recognizerRunnerViewControllerWithOverlayViewController:overlayVC];

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
[self presentViewController:recognizerRunnerViewController animated:YES completion:nil];

As you can see, when initializing MBDocumentOverlayViewController, we are sending delegate property as self. To get results, we need to conform to MBDocumentOverlayViewControllerDelegate protocol.

Using `MBDocumentVerificationOverlayViewController`

MBDocumentVerificationOverlayViewController is built-in overlay view controller which is best suited to use while scanning both sides of a document. Here is an example how to use and initialize MBDocumentVerificationOverlayViewController:

Swift

/** Create your overlay view controller */
let documentOverlayViewController : MBDocumentVerificationOverlayViewController = MBDocumentVerificationOverlayViewController(settings: documentVerificationSettings, recognizerCollection: recognizerCollection, delegate: self)

/** Create recognizer view controller with wanted overlay view controller */
let recognizerRunneViewController : UIViewController = MBViewControllerFactory.recognizerRunnerViewController(withOverlayViewController: documentOverlayViewController)

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
self.present(recognizerRunneViewController, animated: true, completion: nil)

Objective-C

MBDocumentVerificationOverlayViewController *overlayVC = [[MBDocumentVerificationOverlayViewController alloc] initWithSettings:settings recognizerCollection: recognizerCollection delegate:self];
UIViewController<MBRecognizerRunnerViewController>* recognizerRunnerViewController = [MBViewControllerFactory recognizerRunnerViewControllerWithOverlayViewController:overlayVC];

/** Present the recognizer runner view controller. You can use other presentation methods as well (instead of presentViewController) */
[self presentViewController:recognizerRunnerViewController animated:YES completion:nil];

As you can see, when initializing MBDocumentVerificationOverlayViewController, we are sending delegate property as self. To get results, we need to conform to MBDocumentVerificationOverlayViewControllerDelegate protocol.

Custom overlay view controller

Please check our pdf417-sample-Swift for custom implementation of overlay view controller.

Overlay View Controller is an abstract class for all overlay views.

It's responsibility is to provide meaningful and useful interface for the user to interact with.

Typical actions which need to be allowed to the user are:

intuitive and meaniningful way to guide the user through scanning process. This is usually done by presenting a "viewfinder" in which the user need to place the scanned object
a way to cancel the scanning, typically with a "cancel" or "back" button
a way to power on and off the light (i.e. "torch") button

BlinkID SDK always provides it's own default implementation of the Overlay View Controller for every specific use. Your implementation should closely mimic the default implementation as it's the result of thorough testing with end users. Also, it closely matches the underlying scanning technology.

For example, the scanning technology usually gives results very fast after the user places the device's camera in the expected way above the scanned object. This means a progress bar for the scan is not particularly useful to the user. The majority of time the user spends on positioning the device's camera correctly. That's just an example which demonstrates careful decision making behind default camera overlay view.

1. Initialization

To use your custom overlay with MicroBlink's camera view, you must first subclass MBCustomOverlayViewController and implement the overlay behaviour conforming wanted protocols.

2. Protocols

There are five MBRecognizerRunnerViewController protocols and one overlay protocol MBOverlayViewControllerInterface.

Five RecognizerRunnerView protocols are:

In viewDidLoad, other protocol conformation can be done and it's done on recognizerRunnerViewController property of MBOverlayViewController, for example:

Swift and Objective-C

self.scanningRecognizerRunnerViewControllerDelegate = self;

3. Overlay subviews

Developer needs to know which subivew is needed for custom view controller. If you want to use built-in implementation we recommend to use MBModernViewfinderOverlaySubview. In can be initialized in viewDidLoad method:

Swift

viewfinderSubview = MBModernViewfinderOverlaySubview()
viewfinderSubview.moveable = true
view.addSubview(viewfinderSubview)

Objective-C

self.viewfinderSubview = [[MBModernViewfinderOverlaySubview alloc] init];
self.viewfinderSubview.delegate = self.overlaySubviewsDelegate;
self.viewfinderSubview.moveable = YES;
[self.view addSubview:self.viewfinderSubview];

4. Initialization

In Quick Start guide it is shown how to use MBDocumentOverlayViewController. You can now swap MBDocumentOverlayViewController with CustomOverlayViewController

Swift

let recognizerRunnerViewController : UIViewController = MBViewControllerFactory.recognizerRunnerViewController(withOverlayViewController: CustomOverlayViewController)

Objective-C

UIViewController<MBRecognizerRunnerViewController>* recognizerRunnerViewController = [MBViewControllerFactory recognizerRunnerViewControllerWithOverlayViewController:CustomOverlayViewController];

Direct processing API

This guide will in short present you how to process UIImage objects with PDF417.mobi SDK, without starting the camera video capture.

With this feature you can solve various use cases like: - recognizing text on images in Camera roll - taking full resolution photo and sending it to processing - scanning barcodes on images in e-mail etc.

DirectAPI-sample demo app here will present UIImagePickerController for taking full resolution photos, and then process it with MicroBlink SDK to get scanning results using Direct processing API.

Direct processing API is handled with MBRecognizerRunner. That is a class that handles processing of images. It also has protocols as MBRecognizerRunnerViewController. Developer can choose which protocol to conform:

In example, we are conforming to MBScanningRecognizerRunnerDelegate protocol.

Swift

func setupRecognizerRunner() {
    var recognizers = [MBRecognizer]()
    pdf417Recognizer = MBPdf417Recognizer()
    recognizers.append(pdf417Recognizer!)
    let recognizerCollection = MBRecognizerCollection(recognizers: recognizers)
    recognizerRunner = MBRecognizerRunner(recognizerCollection: recognizerCollection)
    recognizerRunner?.scanningRecognizerRunnerDelegate = self
}

func processImageRunner(_ originalImage: UIImage) {
    var image: MBImage? = nil
    if let anImage = originalImage {
        image = MBImage(uiImage: anImage)
    }
    image?.cameraFrame = true
    image?.orientation = MBProcessingOrientation.left
    let _serialQueue = DispatchQueue(label: "com.microblink.DirectAPI-sample-swift")
    _serialQueue.async(execute: {() -> Void in
        self.recognizerRunner?.processImage(image!)
    })
}

func recognizerRunner(_ recognizerRunner: MBRecognizerRunner, didFinishScanningWith state: MBRecognizerResultState) {
    if blinkInputRecognizer.result.resultState == MBRecognizerResultStateValid {
        // Handle result
    }
}

Objective-C

- (void)setupRecognizerRunner {
    NSMutableArray<MBRecognizer *> *recognizers = [[NSMutableArray alloc] init];
    
    self.blinkInputRecognizer = [[MBBlinkInputRecognizer alloc] init];
    
    [recognizers addObject:self.blinkInputRecognizer];
    
    MBRecognizerCollection *recognizerCollection = [[MBRecognizerCollection alloc] initWithRecognizers:recognizers];
    
    self.recognizerRunner = [[MBRecognizerRunner alloc] initWithRecognizerCollection:recognizerCollection];
    self.recognizerRunner.scanningRecognizerRunnerDelegate = self;
}

- (void)processImageRunner:(UIImage *)originalImage {
    MBImage *image = [MBImage imageWithUIImage:originalImage];
    image.cameraFrame = YES;
    image.orientation = MBProcessingOrientationLeft;
    dispatch_queue_t _serialQueue = dispatch_queue_create("com.microblink.DirectAPI-sample", DISPATCH_QUEUE_SERIAL);
    dispatch_async(_serialQueue, ^{
        [self.recognizerRunner processImage:image];
    });
}

#pragma mark - MBScanningRecognizerRunnerDelegate
- (void)recognizerRunner:(nonnull MBRecognizerRunner *)recognizerRunner didFinishScanningWithState:(MBRecognizerResultState)state {
    if (self.blinkInputRecognizer.result.resultState == MBRecognizerResultStateValid) {
        // Handle result
    }
}

Now you've seen how to implement the Direct processing API.

In essence, this API consists of two steps:

Initialization of the scanner.
Call of - (void)processImage:(MBImage *)image; method for each UIImage or CMSampleBufferRef you have.

Using Direct API for `NSString` recognition (parsing)

Some recognizers support recognition from NSString. They can be used through Direct API to parse given NSString and return data just like when they are used on an input image. When recognition is performed on NSString, there is no need for the OCR. Input NSString is used in the same way as the OCR output is used when image is being recognized. Recognition from String can be performed in the same way as recognition from image. The only difference is that user should call - (void)processString:(NSString *)string; on MBRecognizerRunner.

`MBRecognizer` and available recognizers

The `MBRecognizer` concept

The MBRecognizer is the basic unit of processing within the SDK. Its main purpose is to process the image and extract meaningful information from it. As you will see later, the SDK has lots of different MBRecognizer objects that have various purposes.

Each MBRecognizer has a MBRecognizerResult object, which contains the data that was extracted from the image. The MBRecognizerResult object is a member of corresponding MBRecognizer object its lifetime is bound to the lifetime of its parent MBRecognizer object. If you need your MBRecognizerRecognizer object to outlive its parent MBRecognizer object, you must make a copy of it by calling its method copy.

While MBRecognizer object works, it changes its internal state and its result. The MBRecognizer object's MBRecognizerResult always starts in Empty state. When corresponding MBRecognizer object performs the recognition of given image, its MBRecognizerResult can either stay in Empty state (in case MBRecognizerfailed to perform recognition), move to Uncertain state (in case MBRecognizer performed the recognition, but not all mandatory information was extracted) or move to Valid state (in case MBRecognizer performed recognition and all mandatory information was successfully extracted from the image).

As soon as one MBRecognizer object's MBRecognizerResult within MBRecognizerCollection given to MBRecognizerRunner or MBRecognizerRunnerViewController changes to Valid state, the onScanningFinished callback will be invoked on same thread that performs the background processing and you will have the opportunity to inspect each of your MBRecognizer objects' MBRecognizerResult to see which one has moved to Valid state.

As soon as onScanningFinished method ends, the MBRecognizerRunnerViewController will continue processing new camera frames with same MBRecognizer objects, unless paused. Continuation of processing or reset recognition will modify or reset all MBRecognizer objects's MBRecognizerResult. When using built-in activities, as soon as onScanningFinished is invoked, built-in activity pauses the MBRecognizerRunnerViewController and starts finishing the activity, while saving the MBRecognizerCollection with active MBRecognizer.

`MBRecognizerCollection` concept

The MBRecognizerCollection is is wrapper around MBRecognizer objects that has array of MBRecognizer objects that can be used to give MBRecognizer objects to MBRecognizerRunner or MBRecognizerRunnerViewController for processing.

The MBRecognizerCollection is always constructed with array [[MBRecognizerCollection alloc] initWithRecognizers:recognizers] of MBRecognizer objects that need to be prepared for recognition (i.e. their properties must be tweaked already).

The MBRecognizerCollection manages a chain of MBRecognizer objects within the recognition process. When a new image arrives, it is processed by the first MBRecognizer in chain, then by the second and so on, iterating until a MBRecognizer object's MBRecognizerResult changes its state to Valid or all of the MBRecognizer objects in chain were invoked (none getting a Valid result state).

You cannot change the order of the MBRecognizer objects within the chain - no matter the order in which you give MBRecognizer objects to MBRecognizerCollection, they are internally ordered in a way that provides best possible performance and accuracy. Also, in order for SDK to be able to order MBRecognizer objects in recognition chain in a best way possible, it is not allowed to have multiple instances of MBRecognizer objects of the same type within the chain. Attempting to do so will crash your application.

List of available recognizers

This section will give a list of all MBRecognizer objects that are available within PDF417.mobi SDK, their purpose and recommendations how they should be used to get best performance and user experience.

Frame Grabber Recognizer

The MBFrameGrabberRecognizer is the simplest recognizer in SDK, as it does not perform any processing on the given image, instead it just returns that image back to its onFrameAvailable. Its result never changes state from empty.

This recognizer is best for easy capturing of camera frames with MBRecognizerRunnerViewController. Note that MBImage sent to onFrameAvailable are temporary and their internal buffers all valid only until the onFrameAvailable method is executing - as soon as method ends, all internal buffers of MBImage object are disposed. If you need to store MBImage object for later use, you must create a copy of it by calling copy.

Success Frame Grabber Recognizer

The MBSuccessFrameGrabberRecognizer is a special MBecognizer that wraps some other MBRecognizer and impersonates it while processing the image. However, when the MBRecognizer being impersonated changes its MBRecognizerResult into Valid state, the MBSuccessFrameGrabberRecognizer captures the image and saves it into its own MBSuccessFrameGrabberRecognizerResult object.

Since MBSuccessFrameGrabberRecognizer impersonates its slave MBRecognizer object, it is not possible to give both concrete MBRecognizer object and MBSuccessFrameGrabberRecognizer that wraps it to same MBRecognizerCollection - doing so will have the same result as if you have given two instances of same MBRecognizer type to the MBRecognizerCollection - it will crash your application.

This recognizer is best for use cases when you need to capture the exact image that was being processed by some other MBRecognizer object at the time its MBRecognizerResult became Valid. When that happens, MBSuccessFrameGrabberRecognizer's MBSuccessFrameGrabberRecognizerResult will also become Valid and will contain described image.

PDF417 recognizer

The MBPdf417Recognizer is recognizer specialised for scanning PDF417 2D barcodes. This recognizer can recognize only PDF417 2D barcodes - for recognition of other barcodes, please refer to BarcodeRecognizer.

This recognizer can be used in any overlay view controller, but it works best with the MBBarcodeOverlayViewController, which has UI best suited for barcode scanning.

Barcode recognizer

The MBBarcodeRecognizer is recognizer specialised for scanning various types of barcodes. This recognizer should be your first choice when scanning barcodes as it supports lots of barcode symbologies, including the PDF417 2D barcodes, thus making PDF417 recognizer possibly redundant, which was kept only for its simplicity.

You can enable multiple barcode symbologies within this recognizer, however keep in mind that enabling more barcode symbologies affect scanning performance - the more barcode symbologies are enabled, the slower the overall recognition performance. Also, keep in mind that some simple barcode symbologies that lack proper redundancy, such as Code 39, can be recognized within more complex barcodes, especially 2D barcodes, like PDF417.

This recognizer can be used in any overlay view controller, but it works best with the MBBarcodeOverlayViewController, which has UI best suited for barcode scanning.

BlinkInput recognizer

The MBBlinkInputRecognizer is generic OCR recognizer used for scanning segments which enables specifying MBProcessors that will be used for scanning. Most commonly used MBProcessor within this recognizer is MBParserGroupProcessor) that activates all MBParsers in the group to extract data of interest from the OCR result.

This recognizer can be used in any context. It is used internally in the implementation of the provided MBFieldByFieldOverlayViewController.

MBProcessors are explained in The Processor concept section and you can find more about MBParsers in The Parser concept section.

Detector recognizer

The MBDetectorRecognizer is recognizer for scanning generic documents using custom MBDetector. You can find more about Detector in The Detector concept section. MBDetectorRecognizer can be used simply for document detection and obtaining its image. The more interesting use case is data extraction from the custom document type. MBDetectorRecognizer performs document detection and can be configured to extract fields of interest from the scanned document by using Templating API. You can find more about Templating API in this section.