These are the example architectures of live implemented project.

1. Presentation
Layer
Business
Layer
Main Businesses
Meter Registration
Meter Reading
Dynamic Configuration
Authorization
Reporting
User Function Interface
Data Store & Data
Retreival
ICRE(Intelligent
Character
Recognition
Engine)
Portal to Billing
Server
Communication
Web Services
Data StoreServices
Data RetrieveServices
Internal Process
Run ICRE
Receive & Process Input
Produce & Receive Output
Web Service
Intranet Communication
Receive Input fromBO
Forward Input to Billing System
Service
Layer
Data Access
Layer Data Access Layer
Meter Registration Data
Geography Data OtherMeterRegistrationInfo
MeterReading Data
Files
Images Txt files
ICRE Data Other Meter ReadingInfo
Data
Layer
User Data
Figure: Layer based Architecture DiagramofEMRS

2. IP: 192.168.0.13
Billing Server
ORACLE Server
ORACLE Database
EMRS BackOffice Server
Backend
MSSQL DB Server
PHP Web Server
Technology:
 HTML, CSS, Bootstrap
 JavaScript, jQuery
 HTML Helper
Back-OfficePanel
Technology:
 OS: Windows Server
 .NET MVC 4.5
 ASP.NET Web API 2.0
 Visual C#
Technology:
 SQL Server Management
System 2012
 IIS 7.0
App User
Technology:
 C++
 OPENCV
Technology:
 PHP
 XAMP
 Instant client
IP: 192.168.0.13:991
 AnnNetwork.exe
 TraininingNetwork.exe
ICRE
Technology:
 Android
 JSON, XML
 Volley
 SQLITE
Virtual Private Cloud
Figure: Architecture Diagram of EMRS Technology
File Systems

3. Cropped
Image
ArtificialNeuralNetworkBasedDigit
Recognition Engine
Clients
Capture
FullImage
CroppingImage
ANNEngine
BackOfficeServer
APICommunication
CroppedImage
PredictOutput
CroppedImage
PredictOutput
ANNEngine
ANN Input
Generator
.exe
Recognition
.exe
 inputset.txt compare toKnowledge
base ( param.xml )
 Thencalculate withKnowledge
base( param.xml )andmakea
prediction.
 RGB imagetobinaryimage.
 Segmentationto5image.
 InputNormalization
 Scale downto32X32.
 Convert it topixelarrayandsaveitas
a inputset.txt file.
inputset.txt
Output:
Prediction
R&DCenter
TrainingModule
TrainingDataPreparing Module
 Generate trainingset.txt using100 test image foreach
digit.
 Alsogenerate testset.txt using25test image foreach
digit
ANN SystemCreationand
RecognitionModule
 AtfirstgenerateANNlayer(1024 32 10)
 GradientBasedtraining(back propagation)
 Activationfunction(SymmetricalSigmoid)
 Aftercalculateaboveprocessusing trainingset.txt then
generateKnowledge Base( param.xml )
 Thenpredict thegenerated testset.txt withthe created
knowledge.
Recognition Module
EngineEXECreator
ANN InputGenerator exeCreator
 Createexe forANNinput generator.
Recognitionexe Creator
 Createexe forRecognition.
Figure: Intelligent Character Recognition Engine (ICRE) Architecture