Auto Doc ID

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Administrative Functions

Operational functions

Document processing and extraction Derived by keyword rules; see methodology

Problem the AI is intended to solve

The purpose of the tool is to convert inbound images into a standard format that is required for downstream processing for auto document classification and OCR/Data Extraction. After all document processing is completed and we are preparing the final output the images are converted back into a searchable PDF. Auto Doc ID provides suggestions to operators to decrease turn around time and increase quality.

Expected benefits

- Time savings and higher quality - Improved OCR/Data Extraction Accuracy - clearer, properly aligned images increase recognition success for auto classification of Document ID. - Standardization for Automation - converting all documents into TIF IV ensures reliable document classification and processing. - Reduced Manual Corrections - automated alignment, noise removal, and skew correction minimize human intervention. - Preservation of Original Document Integrity - annotations are applied without altering original text, supporting compliance and readability. • Overall, the impacts are limited to operational efficiency and data accuracy. Correct extraction reduces manual data entry for document classification. The AI has no role in determining eligibility, adjudicating claims, or making benefit decisions"

System outputs

Suggested doc ID. It provides document type suggestions in the ImageSort application to guide operators. D3P does not perform content-based analysis but makes technical image-quality determinations, such as: - Detecting and correcting image orientation (auto-rotation) - Identifying and removing image noise (de-speckling) - Determining and correcting crooked images (de-skewing) - Detecting whether images are in color, grayscale, or black-and-white - Preparing images for annotations by adjusting size and margins without altering the original text - All inbound images are standardized into TIF IV format for consistent downstream processing. It does not interpret meaning, generate content, or make adjudicative decisions.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported