Overview

OGRRE provides a web-based graphical user interface (which should function well in any modern web browser) for uploading, reviewing, and editing processed documents. Note: in this page, the term 'OGRRE' is sometimes used as shorthand for the OGRRE User Interface.

Workflow

Below is a summary of the basic workflow for using the OGRRE UI.

Terminology

Below are the terms used in the tool and throughout this guide.

Term	Definition
Project	Shared workspace for working on records
Document Type	Grouping of similar documents, e.g., "well completion report"
Digitize	Intelligently convert an image to corresponding text values
Processor	External tool that reads digitizes the supported types of scanned document images
Record	An uploaded document digitized by the processor
Attribute	One digitized name and value from the document
Confidence	The degree of certainty the tool (or human, if set manually) has in the predicted digitized attribute values

Login to OGRRE

Navigate to the URL of the OGRRE deployment you are using, then login using your Google credentials. You will need to have been added by an administrator as a valid user of this deployed instance.

Upload documents

To upload records, a user must have team_lead or sys_admin role.
Navigate to the record group that you would like to upload documents to. Click on Upload New Record(s). You will see a modal with the processor status, the document upload area, and three upload options:

If the processor is not deployed, click on Deploy Processor. It may take a couple minutes for the processor to deploy.
Once the processor is deployed, choose one of the supported upload methods:

• Upload File: upload one supported file from your machine. Drag a file into the upload area or click Browse files, then click Upload File. Supported file types are TIFF, TIF, PDF, PNG, JPG, JPEG, and ZIP, with a maximum file size of 10 MB.
• Local Directory: upload supported files from a folder on your machine. After selecting a directory, choose how many files to upload, review the file list, and optionally leave Prevent Duplicates enabled so OGRRE skips filenames already present in the current record group.
• GCS Directory: start a batch upload from Google Cloud Storage. Enter the bucket name without gs:// and, if needed, enter a prefix or folder path. Click Check Bucket/Path to confirm how many supported files will be submitted, then click Start Batch Processing.

For all upload methods, the Run cleaning functions switch controls whether OGRRE runs the cleaning functions defined in the processor schema for the uploaded records.

The local directory option shows the selected directory, the number of files that will be uploaded, and duplicate-prevention controls before upload:

The GCS directory option is intended for larger batches that are already staged in a Google Cloud Storage bucket:

When ready, click the upload or batch-processing button for the selected method and document processing will begin.

Review records

Choose a project

Records are organized into projects. Start by selecting the project in the list.

This will open the project view, showing all the records in the project.

The meaning of the columns in this view are as follows:

Column	Description
Record Name	Name of file uploaded
Date Uploaded	When the file was uploaded
API Number	Well API Number available from the uploaded file
Confidence	For each attribute digitized, the associated confidence % given by processor
Mean Confidence	Mean of all digitized attributes’ confidence values in that record
Lowest Confidence	Lowest of all the confidence values in the record
Notes	Notes added to the record
Digitization Status	Status of record in tool: uploading/ processing/ digitized
Review Status	Status of review for the record: unreviewed/ reviewed

Choose a record

Selecting a record from the project (row in the table) to review will open the record details view, which allows review and editing of a single record.

Review/edit record

Below is an example digitized record on the record details view page.

Layout

• The digitized values are on the left scrollable section and the uploaded document is on the right.
• The two sides are linked: selecting attributes on the left will highlight the place where it came from in the document on the right

Contents

The meaning of the columns in the table on the left-hand side are as follows:

Column	Description
Attribute	Name of the attribute in the database (and exported data)
Value	Digitized value detected for this attribute
Confidence	Confidence assigned by the processor. Some attributes with values may have low confidence. Values not found will have 0 confidence.

Actions

• Selecting a row in the table will highlight that attribute value in the image on the right panel.
• Clicking on a value will let you edit the value.
• You can edit and correct any wrong values detected by processor, or add values for attributes not detected.
• Complex tabular attributes are collapsed by default, and expand on clicking the row
• For each record, you could add notes by clicking ‘Notes’ button in the toolbar at the bottom and saving them. You could revisit the notes by clicking on same button again for the record. These notes are also accessible from Records list view.

Keyboard shortcuts:

Windows Key	Mac Key	Action
Up arrow	Up arrow	Previous row in table
Down arrow	Down arrow	Next row in table
Enter	Enter	Edit the value of highlighted attribute, or while editing to save
Esc	Esc	While editing, do not save the edited value
Ctrl + Shift + Right arrow	Cmd + Shift + Right arrow	Mark as reviewed & Go to next record
Ctrl + Left arrow	Cmd + Left arrow	Go to previous record
Ctrl + Right arrow	Cmd + Right arrow	Go to next record

Review status

A record can be in one of the following review statuses:

• Unreviewed
• Incomplete
• Reviewed
• Defective

Export records

You can export all the record data in a project with the Export Project button from any view containing the records table, including the project and record group page.

You can select attributes to include in the exported data.

The CSV option will export the values from each record as a comma-separated values table, whereas the JSON option will export the values along with additional metadata about confidence as a JSON (JavaScript Object Notation) object. Either of these formats can be read easily using Python, and of course CSV is easily imported in Excel, Google Sheets, etc. Additionally, you can choose to export the document image files along with the extracted values.

Exit / Sign out

To logout you can close the window. Since the program uses Google credentials, if you sign out of your Google account you will need to login again on the next visit.