SOLUTION

Automated Data Extraction Solution with Multi-Layered QC

Accurate data extraction plays a critical role in truly reflecting students’ performance. To meet this need, DIGI-TEXX has developed a three-step automated data extraction process that eliminates manual intervention and significantly enhances the reliability of the automated grading process.

1. Image Processing

Before data extraction, DIGI-TEXX applies Image Quality Enhancement technology to optimize the visual quality of test papers, improving readability and the accuracy of information recognition.

This technology leverages digital image processing techniques to:

• Remove background noise and unnecessary blur
• Correct skew and properly rotate the test paper
• Crop out excess or irrelevant areas around the answer sheet
• Adjust brightness, sharpness, and color settings to highlight critical data zones

2. Automated Data Extraction

After the image preprocessing step, the tests are processed by DIGI-XTRACT, an automated document processing platform developed by the DIGI-TEXX software team.

DIGI-XTRACT utilizes Machine Learning (ML) and Deep Learning (DL) models to accurately detect marked answer areas (bubbled answers), even in cases with erasures or corrections.

The system is capable of:

• Identifying and extracting the final selected answers
• Distinguishing clearly between crossed-out, corrected, or lightly shaded choices
• Standardizing the output into a format ready for transfer to the automated grading system

The data is extracted with a confidence level in each field

3. Multi-layered Auto Quality Control

To ensure the extracted results accurately reflect each student’s responses, DIGI-TEXX implements a Multi-layered Auto Quality Control process consisting of two to three levels:

• Level 1 – Automated Scoring: Executes quality control based on confidence levels, applying a complex scoring method to ensure the highest possible automated output quality.
• Level 2 – Randomized Verification: A reviewer performs random quality checks on machine-verified data to detect potential algorithmic oversights and ensure consistent accuracy across the dataset.
• Level 3 – Targeted Expertise (if required): For exams with abnormal indicators or low-confidence levels, the data is escalated to a second reviewer for deeper inspection.

As a part of the Auto Quality Control process, we integrate detailed validation criteria to ensure input data accuracy and consistency, including:

• Format validation: Ensures fields like student ID, test code, and date follow required formats.
• Data logic checks: Verifies logical consistency across fields, such as making sure the total number of selected answers doesn’t exceed the number of questions
• Image quality re-assessment: Ensures that issues such as blurriness, skewing, or incorrect page size do not compromise the integrity of the extracted data.

We ensure data quality and accuracy through Multi-layered Quality Control

This process is also integrated with detailed validation criteria to ensure input data accuracy and consistency, including:

• Format validation: Ensures fields like student ID, test code, and date follow required formats.
• Data logic checks: Verifies logical consistency across fields, such as making sure the total number of selected answers doesn’t exceed the number of questions.

Image quality re-assessment: Ensures that issues such as blurriness, skewing, or incorrect page size do not compromise the integrity of the extracted data.