Case Study- Broadcast Tamil Closed Captioning Services for Television Content
About the Client
One of India’s leading entertainment and television broadcasting companies partnered with Filose to manage Tamil closed captioning services for Tamil-language television programs. The client required highly accurate, broadcast-ready captions delivered within strict daily timelines while ensuring compliance with technical broadcast standards.
The project demanded a scalable solution capable of handling continuous content delivery with speed, accuracy, and secure file management for broadcast captioning services.
Client Requirements for Closed Captioning Services
The client required a comprehensive closed captioning workflow that could support daily television broadcasting schedules while maintaining high linguistic and technical accuracy.
Key Requirements Included:
- Accurate Tamil-language transcription
- Proper subtitle segmentation and synchronization
Required Delivery Formats:
- Compliance with broadcast captioning standards
- Multi-format caption file delivery
- Strict turnaround timelines
- Secure handling of media assets and caption files
- .SRT
- .TXT
- .FPC
- .STL
- .W32
The client typically shared .mp4 video files only 1–2 days before broadcast, while finalized caption files including Tamil SRT outputs had to be delivered at least 24 hours before airing.
Project Details
To address the client’s requirements efficiently, Filose implemented a hybrid workflow combining AI-powered automation, Python-based processing, human transcription expertise, and multi-level quality assurance.
The project currently supports two television programs, with three episodes broadcast daily from Monday to Saturday. Given the high-volume and fast-paced nature of broadcast media, the workflow was designed to optimize both speed and quality.
The end-to-end workflow begins with secure file intake via Filose’s protected server infrastructure. Once the video files are received, the technical team initiates an automated processing pipeline built using AI transcription engines and Python scripts.
The automation layer performs:
- Speech-to-text transcription
- Subtitle segmentation
- Timing synchronization
- Initial generation of Tamil SRT files
After extensive testing and optimization, the AI transcription engine achieved approximately 75% baseline transcription accuracy, significantly reducing manual effort and improving processing speed.
The generated subtitle files are then assigned to trained Tamil-language transcription specialists who perform detailed editing and contextual corrections. Human reviewers ensure:
- Linguistic accuracy
- Proper subtitle segmentation
- Timing synchronization with video
- Compliance with the client’s captioning guidelines
Once editing is completed, the files undergo multiple levels of QA validation, including both manual and automated checks.
Services:
- Closed Captioning Services
- Tamil Transcription & Subtitle Editing
- Broadcast Subtitle Localization
- AI-Assisted Captioning
- Multi-format Subtitle Conversion
- Broadcast QA & Validation
Challenges in Broadcast Captioning Services
The project presented several challenges in broadcast captioning services:
Tight Broadcast Timelines
Episodes were shared only 1–2 days before broadcast, requiring rapid processing and delivery within strict deadlines.
Broadcast-Level Accuracy Requirements
Closed captions had to meet high linguistic and technical quality standards suitable for television broadcast.
Multi-format Deliverables
The client required caption files in multiple broadcast-compatible formats, increasing processing complexity.
Subtitle Timing & Segmentation
Captions needed precise synchronization with spoken dialogue and adherence to subtitle display standards.
Broadcast Timecode Compliance
All files had to begin from the client’s required broadcast timecode of 10:00:00:00 instead of the standard zero-hour format.
Quality Assurance at Scale
Managing daily episode delivery while maintaining consistent quality required a robust QA framework.
Solutions Using AI Transcription Services for Tamil Closed Captioning
To overcome these challenges, Filose developed a scalable hybrid workflow integrating automation with human linguistic expertise.
AI + Human Hybrid Workflow
AI automation accelerated transcription and segmentation, while experienced Tamil linguists refined captions for contextual accuracy and broadcast compliance.
Python-Based Automation
Custom Python scripts automated:
- Subtitle segmentation
- Technical QA validation
- Timecode shifting
- Formatting compliance checks
Multi-Level Quality Assurance
The project incorporated several QA checkpoints:
- AI transcription validation
- Human transcription editing
- Random segment QA verification
- Automated technical QA reports
- PM-level review
- Final broadcast QA validation
Broadcast Timecode Automation
Python scripts automatically shifted subtitle files from standard timecode to the client’s required 10-hour broadcast format.
Secure Infrastructure
All file exchanges were managed through Filose’s secure server infrastructure, ensuring confidentiality and secure delivery.
Results of Tamil Closed Captioning Success
The hybrid automation model delivered measurable operational and quality improvements.
Project Outcomes:
Successfully delivered 40+ broadcast episodes in one month
Managed two episodes per day, six days a week
Achieved 99% issue-free broadcast delivery
Maintained high transcription accuracy
Reduced manual transcription effort significantly
Improved turnaround time through automation
Ensured consistent compliance with broadcast standards
The combination of AI-driven automation and human expertise enabled scalable, reliable, and cost-efficient closed captioning operations for daily television broadcasting.
Why Filose for Closed Captioning Services?
Looking for AI-assisted localization, subtitle localization, transcription, or broadcast captioning solutions? Connect with us to explore scalable language technology and media localization solutions tailored to your business requirements at sales@filose.com.
