The current release for all versions is 2019.2 (version 2.2.0)

Next release will be 2019.3 (version 2.3.x) (scheduled to be released mid Q4 2019)

Preprocessing is making image noise reduction, binarization, image emphasis, skew correction, and document orientation detection.

Detection phase is our optimized set of algorithms that analyze the image and detects areas with letters in patterns specified in BIC and ILU standards. Additionally, detected area is cleaned and noise removed.

Detected areas on the image are recognized and assigned a confidence factor. In case multiple recognitions each candidate is assigned a confidence factor. The confidence factor indicates how confident OCR engine was in the final choice of container code.

Recognition results are returned as JSON response and can be used in further processing or exported. The developer has full control over the OCR results.

For activation of a license, there should be access to the ConPDS licensing server via Internet (TCP port 443). Once activated, there is no longer such requirement unless new license key has to be applied and activated.

Images should be in JPEG format (https://en.wikipedia.org/wiki/JPEG).

For optimal recognition speed we recommend bellow resolutions.

 

Aspect ratio	Resolutions (HxW or WxH) *
4:3	1024×768, 1280×960, 1400×1050, 1440×1080
16:10	1280×800, 1440×900, 1680×1050

*) Lower resolutions are supported but results might not be as accurate as possible. Higher resolutions are also supported, but it will result in larger files which results in slower recognition.

Import library

• Copy aar file to the folder ‘libs’ of your project
• Add dependency on file to build.gradle file of your project
  • compile (name:‘licensed-ocr-engine-release’, ext:’aar’)

Extend or specify App class

• Please specify the App class in the tag name in the Manifest of your project:

  <application
  android:name=".App"
  ...

• If you already have the custom class that extends Application class and is specified in android:name tag then please extend your custom application class from App:

  public class CustomApp extends App {
  ...
  <application
  android:name=".CustomApp"
  ...

Recognize

• Create an instance of the class MainInteractorImpl
• Call the method doRecognize of the created instance and pass the next arguments to the method
  • API Key
  • Licensed API Key
  • Array of paths to the photos to be recognized
  • Implementation of interface MainInteractor.Callback, that has the methods, described in Process results of recognizing.

Process results of recognizing

There are two methods, that may be called during recognize process.

• recogOk(Map<String, String> recognizedData)
  • recognizedData map has results. Each entry of the map consist of {key} – {path-to-file} and {value} – {resultof-recognizing}
• recogError(String msg)
  • This method is called when an error has been occurred in license checking process.
  • Possible values of parameter ‘msg’
  • “Invalid API key”
  • “License request requires approval”
  • “The license is blocked”
  • “The license is not valid”

Download sample project here (coming soon)

Import library

• Add .framework file to project
• Add .framework file to “Linked frameworks and libraries” to project target

Licensing

• Use static instance of class License
• Use method ‘getPublicKeyFromServer:completion’ to get server public key. Completion contains public key (optional) and error (optional)
• Use method ‘sendLicenseRequest:serverPublicKey:apiKey:licenseApiKey:completion’ to get information about license. Completion contains instance of class LicenseInfo(optional) and error (optional)

Recognize

• Create instance of class RecognitionWrapper
• Call method ‘recognizeCodes’
• The method returns pointer to instance of NSArray. It is ‘nil’ if recognizing failed. If recognition is successful, it contains two strings: container number and ISO size and type code.

Download sample project here (coming soon)

Library offers following methods

• string ConPDS.Net.Proxy.RecogFromBMPToJSON(System.Drawing.Image image)
  • Input parameter – picture for recognition
  • Function returns a string in json format (JSON structure. Click here for description)

• ContainerCodeData ConPDS.Net.Proxy.RecogFromBMP(System.Drawing.Image image)
  • Input parameter image – picture for recognition
  • Function returns a Container Code/ISO Size & Type (structure. Should be managed by developer)

Example of usage

private void btnLoad_Click(object sender, RoutedEventArgs e)
{
   OpenFileDialog op = new OpenFileDialog();
   op.Title = "Select a picture";
   op.Filter = "All supported graphics|*.jpg;*.jpeg;*.png|" +
   "JPEG (*.jpg;*.jpeg)|*.jpg;*.jpeg|" +
   "Portable Network Graphic (*.png)|*.png";
   if (op.ShowDialog() == true)
   {
      imgPhoto.Source = new BitmapImage(new Uri(op.FileName));

      System.Drawing.Image img = System.Drawing.Image.FromFile(op.FileName);
      ContainerCodeData recogStructure = ConPDS.Net.Proxy.RecogFromBMPToJSON(img);
      if (recogStructure.Result == null)
      {
      textValue.Text = "<text not Recognized>";
      }
      else
      {
      textValue.Text = recogStructure.Result;
      }
   }
}

Download C# sample project source code

Download C# sample project – compiled version

From the download link, you received, download and save the conPDS.jar file in any folder or your choice. Create new or modify config.properties file in same folder as the conPDS.jar file and insert the api_key and license_key provided to you via email.

Example config.properties file:
api_key=cc62f4b0-ccf1-44ac-b965-24b9cfcf7bfb
license_key=240bac2-58fb-4178-b21d-a4df3edab338

The conPDS.jar is a java console application and accessed from the command line. On the first launch, application checks if the API and license keys are valid and activated. If not it contacts the ConPDS Licensing Server to get an activation key. Activation keys are then stored locally in a subfolder called \conpds. On the first launch, the application creates two subfolders: \incoming and \completed You have four options to process photos:

1. Store photos inside the folder \incoming and call command line: java –jar conPDS.jar
2. Call command line: java –jar conPDS.jar {path_to_photo}\{file_name}
3. Call command line: java –jar conPDS.jar -listen 9999
4. Call command line: java –jar conPDS.jar -monitor

Option 1 …
processes all photos in \incoming and moves them to \completed folder.

Option 2 …
processes a single specified photo only.

Option 3 …
launches app in listen mode ready for socket connection. This mode is used if you don’t want to save images onto disk and then process but instead just load the binary stream of the image directly into OCR engine and it will be processed just like it was a file. Port number (9999) can be any port of your choice. Please download code snippet for how to use socket connection.

Option 4 …
launches app in monitoring mode. This mode will automatically process all image saved inside \incoming folder and move them to \completed folder.

NOTE: Option 3 and 4 can be combined. In all situations, a new folder called \logs is created and will contain a log file with processing results. Log files are named with the current date. You need to write your code and logic to read output whether it is to log file or console. In both situations, a new folder called \logs is created and contains a log file with processing results. Log files are named with the current date. You need to write your code and logic to read output whether it is to log file or console. Results are returned as JSON response and will be saved into logfile or printed in the console.

Calling java –jar ConPDS.jar –help prints below output:

Usage: java -jar conPDS.jar [-options] [path_to_image]
-version    prints application compatible OS and version and exit
-id          prints application ID and exit
-listen      launch app in listen mode on specified port number
-monitor     launch app in monitor mode
-nomove      leaves photo/-s in original place instead of moving into /completed folder
-nolog       don’tgenerate logfile

Download sample code for using socket connection (download here)

From the download link, you received, download and save the conPDS.jar file in any folder or your choice. Create new or modify config.properties file in same folder as the conPDS.jar file and insert the api_key and license_key provided to you via email.

Example config.properties file:
api_key=cc62f4b0-ccf1-44ac-b965-24b9cfcf7bfb
license_key=240bac2-58fb-4178-b21d-a4df3edab338

The conPDS.jar is a java console application and accessed from the command line. On the first launch, application checks if the API and license keys are valid and activated. If not it contacts the ConPDS Licensing Server to get an activation key. Activation keys are then stored locally in a subfolder called /conpds. On the first launch, the application creates two subfolders: /incoming and /completed You have four options to process photos:

1. Store photos inside the folder /incomingand call command line: java –jar conPDS.jar
2. Call command line: java –jar conPDS.jar {path_to_photo}/{file_name}
3. Call command line: java –jar conPDS.jar -listen 9999
4. Call command line: java –jar conPDS.jar -monitor

Option 1 …
processes all photos in /incoming and moves them to /completed folder.

Option 2 …
processes a single specified photo only.

Option 3 …
launches app in listen mode ready for socket connection. This mode is used if you don’t want to save images onto disk and then process but instead just load the binary stream of the image directly into OCR engine and it will be processed just like it was a file. Port number (9999) can be any port of your choice. Please download code snippet for how to use socket connection.

Option 4 …
launches app in monitoring mode. This mode will automatically process all image saved inside /incoming folder and move them to /completed folder.

NOTE: Option 3 and 4 can be combined. In all situations, a new folder called /logs is created and will contain a log file with processing results. Log files are named with the current date. You need to write your code and logic to read output whether it is to log file or console. In both situations, a new folder called /logs is created and contains a log file with processing results. Log files are named with the current date. You need to write your code and logic to read output whether it is to log file or console. Results are returned as JSON response and will be saved into logfile or printed in the console.

Calling java –jar ConPDS.jar –help prints below output:

Usage: java -jar conPDS.jar [-options] [path_to_image]
-version     prints application compatible OS and version and exit
-id              prints application ID and exit
-listen        launch app in listen mode on specified port
-monitor     launch app in monitor mode
-nomove    leavesphoto/-sin original place instead of moving into /completed folder
-nolog        don’tgenerate logfile

Download sample code for using socket connection (download here)

Download swagger definition here (download here)

To protect your investment, it is mandatory that you purchase one initial year of Solution Assurance with each license purchase. Solution Assurance unlocks access to Technical Support, Product Updates, and Upgrades. The Solution Assurance is extended automatically at the end of each 12 month period, and this is done automatically unless, you as a customer, specify otherwise.

Product Upgrades and Service Releases – To upgrade your version of ConPDS OCR engine to the latest available version, you can download from the download link provided when purchasing the product. If you no longer have the link, please contact ConPDS Support by sending email to support@conpds.com.