q-Board
q-Board
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Automated Loan Boarding
Automated Loan Boarding
Automated Loan Boarding
Automated Loan Boarding
The objective of this project is to integrate Optical Character Recognition (OCR) and Machine Learning (ML) technologies into the application to automate the process of updating information from PDF documents directly into the application fields. This will streamline data entry processes, reduce manual errors, and improve overall efficiency.
The objective of this project is to integrate Optical Character Recognition (OCR) and Machine Learning (ML) technologies into the application to automate the process of updating information from PDF documents directly into the application fields. This will streamline data entry processes, reduce manual errors, and improve overall efficiency.
The objective of this project is to integrate Optical Character Recognition (OCR) and Machine Learning (ML) technologies into the application to automate the process of updating information from PDF documents directly into the application fields. This will streamline data entry processes, reduce manual errors, and improve overall efficiency.
The objective of this project is to integrate Optical Character Recognition (OCR) and Machine Learning (ML) technologies into the application to automate the process of updating information from PDF documents directly into the application fields. This will streamline data entry processes, reduce manual errors, and improve overall efficiency.
Scope
Scope
Scope
Scope
The scope of this project includes the development and implementation of OCR and ML algorithms within the application framework. Specifically, the system will be able to:
The scope of this project includes the development and implementation of OCR and ML algorithms within the application framework. Specifically, the system will be able to:
Identify and select relevant information from PDF documents. Eg if we have 500 pages bunch. Only 10 Document need to be identify and scanned, those document name are as follow:
Identify and select relevant information from PDF documents. Eg if we have 500 pages bunch. Only 10 Document need to be identify and scanned, those document name are as follow:
Identify and select relevant information from PDF documents. Eg if we have 500 pages bunch. Only 10 Document need to be identify and scanned, those document name are as follow:
Identify and select relevant information from PDF documents. Eg if we have 500 pages bunch. Only 10 Document need to be identify and scanned, those document name are as follow:
First Lien Doc (Deed of Trust)
Second Lien Doc (2nd Deed of Trust)
Allonge to Note
Modification of Mortgage
Equity Line Credit Agreement
Assignment of Mortgage
Promissory Note
Adjustable Rate Note
Success & Entrent Deed
Title Doc
Notice of Foreclose
These documents may change based on client requirement or Loan requirement.
OCR technology will read the information and Copy the selected information from the PDF.
Select the corresponding field where to Paste the copied information OCR Technology will Paste the information into the corresponding fields within the application.
Implement manual verification by the processor to ensure accuracy, correcting any spelling mistakes or special characters encountered during the process.
First Lien Doc (Deed of Trust)
Second Lien Doc (2nd Deed of Trust)
Allonge to Note
Modification of Mortgage
Equity Line Credit Agreement
Assignment of Mortgage
Promissory Note
Adjustable Rate Note
Success & Entrent Deed
Title Doc
Notice of Foreclose
These documents may change based on client requirement or Loan requirement.
OCR technology will read the information and Copy the selected information from the PDF.
Select the corresponding field where to Paste the copied information OCR Technology will Paste the information into the corresponding fields within the application.
Implement manual verification by the processor to ensure accuracy, correcting any spelling mistakes or special characters encountered during the process.
Functional Requirements
Functional Requirements
Functional Requirements
Functional Requirements
OCR Integration
OCR Integration
OCR Integration
OCR Integration
The system shall be capable of extracting text from PDF documents accurately using OCR technology.
It shall identify and highlight the relevant information within the PDF.
The processor shall have the option to select and confirm the highlighted information for extraction.
The system shall be capable of extracting text from PDF documents accurately using OCR technology.
It shall identify and highlight the relevant information within the PDF.
The processor shall have the option to select and confirm the highlighted information for extraction.
ML Integration
ML Integration
ML Integration
ML Integration
The ML algorithm shall analyze the extracted text to identify the appropriate application fields for data entry.
It shall automate the process of pasting the extracted information into the corresponding fields within the application.
The ML algorithm shall analyze the extracted text to identify the appropriate application fields for data entry.
It shall automate the process of pasting the extracted information into the corresponding fields within the application.
Manual Verification
Manual Verification
Manual Verification
Manual Verification
The processor shall manually review the extracted information for accuracy.
They shall correct any spelling mistakes or special characters encountered during the extraction process.
The processor shall manually review the extracted information for accuracy.
They shall correct any spelling mistakes or special characters encountered during the extraction process.
Non-Functional Requirements
Non-Functional Requirements
Non-Functional Requirements
Non-Functional Requirements
Accuracy
Accuracy
Accuracy
Accuracy
The OCR and ML algorithms shall strive for a high level of accuracy in extracting and updating information.
The manual verification process shall serve as a quality control measure to ensure data accuracy.
The OCR and ML algorithms shall strive for a high level of accuracy in extracting and updating information.
The manual verification process shall serve as a quality control measure to ensure data accuracy.
Performance
Performance
Performance
Performance
The system shall perform efficiently, with minimal latency in processing PDF documents and updating application fields.
The system shall perform efficiently, with minimal latency in processing PDF documents and updating application fields.
User Interface
User Interface
User Interface
User Interface
The user interface shall be intuitive and user-friendly for both processors and administrators.
It shall provide clear instructions and feedback during the OCR and ML integration process.
The user interface shall be intuitive and user-friendly for both processors and administrators.
It shall provide clear instructions and feedback during the OCR and ML integration process.
Assumptions and Constraints
Assumptions and Constraints
Assumptions and Constraints
Assumptions and Constraints
Assumptions
Assumptions
Assumptions
Assumptions
The PDF documents provided will be of standard format and layout, facilitating accurate text extraction.
Processors will have the necessary training to perform manual verification effectively.
Documents may vary in format and layout, potentially posing challenges for accurate extraction.
The PDF documents provided will be of standard format and layout, facilitating accurate text extraction.
Processors will have the necessary training to perform manual verification effectively.
Documents may vary in format and layout, potentially posing challenges for accurate extraction.
Constraints
Constraints
Constraints
Constraints
The system's performance may be affected by the quality and clarity of the PDF documents.
Integration with legacy systems or third-party applications may pose compatibility challenges.
The system's performance may be affected by the quality and clarity of the PDF documents.
Integration with legacy systems or third-party applications may pose compatibility challenges.
Risks and Mitigation
Risks and Mitigation
Risks and Mitigation
Risks and Mitigation
Risks
Risks
Risks
Risks
Inaccurate OCR or ML processing leading to data entry errors.
Insufficient manual verification resulting in overlooked mistakes.
Inaccurate OCR or ML processing leading to data entry errors.
Insufficient manual verification resulting in overlooked mistakes.
Mitigation
Mitigation
Mitigation
Mitigation
Regular testing and validation of OCR and ML algorithms to improve accuracy.
Implementing a robust manual verification process with adequate training for processors.
Regular testing and validation of OCR and ML algorithms to improve accuracy.
Implementing a robust manual verification process with adequate training for processors.
Explore Options With No Cost
Explore Options With No Cost
Explore Options With No Cost
Explore Options With No Cost
13492 Research Blvd
#120 Austin,
TX 78750
(512) 893 7797
sales@acuriq.com
13492 Research Blvd
#120 Austin,
TX 78750
(512) 893 7797
sales@acuriq.com
13492 Research Blvd
#120 Austin,
TX 78750
(512) 893 7797
sales@acuriq.com
13492 Research Blvd
#120 Austin,
TX 78750
(512) 893 7797
sales@acuriq.com
13492 Research Blvd
#120 Austin,
TX 78750
(512) 893 7797
sales@acuriq.com