Architecting Screen Recording Indexing Services for Full-Text Search of Captured Content

How-To-Articles
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Architecting Screen Recording Indexing Services for Full-Text Search of Captured Content

What This Guide Covers

This guide details the architectural pattern for ingesting Genesys Cloud Interaction Recording screen captures, extracting text via Optical Character Recognition (OCR), and indexing that content for low-latency full-text search. The end result is a system where agents can query historical screen interactions by keywords found within the visual context of the recording, rather than relying solely on audio transcripts or metadata tags.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 3 (or higher) with Interaction Recording and Speech Analytics add-ons.
  • Permissions:
    • Interaction Recording > Edit
    • Integration > Connect > Edit (for creating the Connector)
    • User > Edit (to assign the connector to users)
  • External Dependencies:
    • An object storage bucket (AWS S3, Azure Blob, or Google Cloud Storage) with private access.
    • An OCR service (AWS Textract, Azure Computer Vision, or Google Document AI).
    • A search index backend (Elasticsearch, OpenSearch, or Algolia).
    • A serverless compute environment (AWS Lambda, Azure Functions, or Google Cloud Functions) to orchestrate the pipeline.

The Implementation Deep-Dive

1. Configuring the Interaction Recording Connector

The foundation of this architecture is the Connector object in Genesys Cloud. This object defines how recording data is exported from Genesys Cloud to your external storage. Most implementations default to S3-compatible storage, but the configuration nuances determine the viability of downstream processing.

Step 1.1: Create the Connector

Navigate to Admin > Integrations > Connectors. Click Add Connector. Select S3 Compatible Storage as the type.

  • Name: PROD-REC-SCR-INDEXING
  • Bucket Name: gen-rec-screens-prod
  • Region: Match your Genesys Cloud deployment region (e.g., us-east-1 for US East).
  • Access Key/Secret Key: Use an IAM role or user with s3:PutObject permissions. Do not use root credentials.

Step 1.2: Configure Recording Settings

Click Next to reach the recording settings. This is where the first critical architectural decision occurs.

  • Recording Type: Select Screen.
  • Storage Format: Select MP4.
  • Resolution: Select 720p.

The Trap: Selecting 1080p or 4K for screen recordings intended for OCR indexing.
Why it fails: Higher resolutions exponentially increase file size and storage costs without providing proportional gains in OCR accuracy for standard UI elements. More critically, it increases the latency of the OCR pipeline. A 10-minute 1080p screen recording may be 1.5 GB, whereas 720p is approximately 300 MB. If your OCR service charges by page or character, or has rate limits on file size, 1080p will cause pipeline failures or cost overruns. For full-text search, the goal is character recognition, not archival fidelity. 720p provides sufficient pixel density for Tesseract or AWS Textract to read standard font sizes (12pt+) used in enterprise applications.

Step 1.3: Define the Storage Path

Configure the storage path to include unique identifiers. Use the following template:

{organizationId}/{userId}/{recordingId}/{recordingId}.mp4

Architectural Reasoning: Including {userId} in the path structure allows for efficient prefix-based filtering during the ingestion phase. If you need to re-index a specific agent’s history, you can scan only their prefix. Including {recordingId} ensures idempotency; if the connector retries a failed upload, it overwrites the same file rather than creating duplicates.

2. Building the Ingestion and OCR Pipeline

Once the recording is stored in S3, Genesys Cloud is done. The responsibility shifts to your custom infrastructure. You must build an event-driven pipeline that triggers when a new .mp4 file appears.

Step 2.1: Event Trigger Mechanism

Use S3 Event Notifications to trigger a Lambda function (or equivalent serverless function) when an object is created.

  • Event Type: s3:ObjectCreated:*
  • Filter Prefix: */ (to catch all user directories)

The Trap: Using a polling mechanism (e.g., a cron job scanning S3 every 5 minutes).
Why it fails: Polling introduces significant latency. If an agent needs to search a recording immediately after a call, a 5-minute delay is unacceptable. Event-driven architectures provide sub-second trigger latency. Furthermore, polling at scale (thousands of recordings per hour) creates unnecessary API calls and costs.

Step 2.2: Frame Extraction Strategy

Video files cannot be directly OCR’d. You must extract frames. The challenge is determining which frames to process. Processing every frame is computationally prohibitive.

Architectural Decision: Use Keyframe Extraction combined with Content Change Detection.

  1. Download the MP4 to the Lambda function’s temporary storage (/tmp).
  2. Use FFmpeg (installed in the Lambda layer) to extract frames.
  3. Do not extract at a fixed interval (e.g., 1 frame per second). Instead, extract frames where visual content changes significantly.

Code Snippet: FFmpeg Command for Adaptive Frame Extraction

ffmpeg -i /tmp/input.mp4 -vf "select='gt(scene,0.3)',showinfo" -vsync vfr /tmp/frame_%04d.jpg

Explanation:

  • select='gt(scene,0.3)': Extracts frames where the scene change score is greater than 0.3. This ignores static screens (e.g., a form left open for 30 seconds) and captures only moments when the agent navigates or updates data.
  • showinfo: Logs metadata for debugging.
  • vsync vfr: Variable frame rate ensures no duplicate frames are saved.

The Trap: Extracting frames at a fixed rate (e.g., 1 fps) regardless of content.
Why it fails: In a screen recording, 90% of the frames are identical or nearly identical (static UI). Processing these duplicates wastes OCR compute resources and bloats the index with redundant data. Scene detection reduces the frame count by 60-80% while retaining all unique textual information.

Step 2.3: OCR Processing

Send the extracted JPEG frames to an OCR service. For enterprise-scale accuracy, use AWS Textract or Azure Computer Vision Read API.

Code Snippet: AWS Textract Integration (Python/Boto3)

import boto3
import json

def process_frame_with_textract(frame_path):
    textract = boto3.client('textract')
    
    with open(frame_path, 'rb') as image:
        response = textract.detect_text(Image={'Bytes': image.read()})
        
    # Extract text blocks
    text_blocks = [block['Text'] for block in response['Blocks'] if block['BlockType'] == 'WORD']
    return ' '.join(text_blocks)

Architectural Reasoning: Using detect_text instead of analyze_document for screen recordings. analyze_document is optimized for forms and tables, which adds latency and cost. Screen recordings are primarily free-form text (UI labels, data fields, chat messages). detect_text is faster and cheaper for this use case. If your specific use case involves heavy form processing (e.g., insurance claims), switch to analyze_document to capture key-value pairs.

3. Indexing for Full-Text Search

The raw text from OCR is noisy. It contains UI labels (“Submit”, “Cancel”), random data, and potential PII. You must clean and index this data effectively.

Step 3.1: Text Cleaning and Normalization

Before indexing, apply the following transformations:

  1. Remove Stop Words: Filter out common UI words (“File”, “Edit”, “View”, “Help”).
  2. Normalize Case: Convert all text to lowercase.
  3. Remove Special Characters: Strip non-alphanumeric characters except for hyphens and underscores (common in IDs).
  4. PII Redaction: If searching for PII is not required, redact SSNs, credit card numbers, and names using a regex-based sanitizer or a dedicated PII detection service.

The Trap: Indexing the raw OCR output without cleaning.
Why it fails: Search relevance degrades rapidly. If an agent searches for “Invoice 12345”, they do not want results containing the word “Invoice” from the browser toolbar or menu bar. Indexing only the “content area” of the screen (via coordinate filtering from Textract) improves relevance. Textract returns bounding boxes; you can filter out text from the top 10% and left 5% of the image, which typically contains browser chrome and OS taskbars.

Step 3.2: Elasticsearch Index Structure

Design the Elasticsearch index to support fast retrieval and filtering.

JSON Mapping Example:

{
  "mappings": {
    "properties": {
      "recording_id": { "type": "keyword" },
      "user_id": { "type": "keyword" },
      "timestamp": { "type": "date" },
      "text_content": { 
        "type": "text", 
        "analyzer": "standard" 
      },
      "frame_number": { "type": "integer" },
      "confidence_score": { "type": "float" },
      "pii_detected": { "type": "boolean" }
    }
  }
}

Architectural Reasoning: Storing frame_number allows you to reconstruct the timeline. When a search result is returned, you can jump to the specific timestamp in the video player corresponding to that frame. Storing confidence_score allows you to filter out low-quality OCR results, reducing false positives in search.

Step 3.3: Linking to Genesys Cloud Interaction

To make the search useful, you must link the indexed text back to the Genesys Cloud interaction.

  1. Retrieve the recording_id from the S3 path.
  2. Use the Genesys Cloud API to fetch the interaction details.

API Call:

GET /api/v2/recordings/{recordingId}
Authorization: Bearer <token>

Response Handling:
Extract the interactionId from the response. Store this interactionId in the Elasticsearch document. This allows you to display the search result alongside standard interaction metadata (agent name, queue, duration) fetched from Genesys Cloud.

The Trap: Storing all interaction metadata in Elasticsearch.
Why it fails: Data duplication and inconsistency. If an agent’s name changes in Genesys Cloud, the index becomes stale. Store only the interactionId and recordingId in the search index. Fetch the rich metadata from Genesys Cloud at query time. This keeps the index lean and ensures data accuracy.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Low-Contrast or Blurred Text

The Failure Condition: The OCR service returns empty strings or gibberish for frames containing small text or low-contrast UI elements (e.g., dark mode interfaces).

The Root Cause: Standard OCR engines assume high-contrast, printed text. Screen recordings often contain dynamic content, low-resolution fonts, or UI elements that are not optimized for OCR.

The Solution: Implement a confidence threshold filter. If the average confidence score of words in a frame is below 80%, discard the frame. Additionally, use image preprocessing before OCR. Apply contrast enhancement and noise reduction using OpenCV.

import cv2

def preprocess_image(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Apply adaptive thresholding to enhance contrast
    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                   cv2.THRESH_BINARY, 11, 2)
    return thresh

Edge Case 2: Large-Volume Backlogs

The Failure Condition: During peak hours, the S3 bucket receives hundreds of recordings per minute. The Lambda functions hit concurrency limits, causing processing delays of several hours.

The Root Cause: Serverless functions have default concurrency limits (e.g., 1,000 concurrent executions in AWS Lambda). If the ingestion rate exceeds this, events queue up in SQS (if decoupled) or fail.

The Solution: Implement a SQS Decoupling Layer. Configure S3 to send notifications to an SQS queue instead of directly invoking Lambda. Increase the SQS queue’s visibility timeout and configure the Lambda function to poll the queue with a batch size of 10. This smooths out traffic spikes and prevents cold-start bottlenecks. Monitor the ApproximateNumberOfMessagesVisible metric to alert on backlogs.

Edge Case 3: PII Leakage in Search Results

The Failure Condition: An agent searches for a customer name and finds the name in the OCR text, violating HIPAA or GDPR.

The Root Cause: The OCR pipeline indexed all text, including sensitive data fields.

The Solution: Implement PII Redaction at Ingestion. Before indexing, pass the extracted text through a PII detection service (e.g., AWS Comprehend Medical or Azure Text Analytics). Replace detected PII with placeholders ([SSN], [NAME]). If the use case requires searching for PII, restrict access to the index using Role-Based Access Control (RBAC) in Elasticsearch, ensuring only authorized users can query PII fields.

Official References