In the world of artificial intelligence (AI) and machine translation (MT), training models to accurately process and interpret data requires diverse and high-quality datasets. One company that excels in this area is Flitto, a Korean firm with over a decade of expertise in live text collection. Using devices such as iPhones and iPads, they deliver data in formats like .HEIC and .MOV, showcasing how companies collect and curate datasets essential for AI training.
Flitto also has an online translation portal which you can use to translate texts, files, images and YouTube subtitles. They also offer a paid human proofreading service (PEMT). You can check it out here.
Let’s delve into how each collection category contributes to advancing MT and AI systems.
Curved Text: Training AI to Handle Complex Geometries
What It Involves: Curved text refers to text that follows a non-linear path, often appearing in images where text is printed on bottles, cans, or spherical surfaces. More than one-third of the image contains curved text in this collection category.
AI Challenges and Benefits: Traditional OCR (Optical Character Recognition) systems struggle with curved text due to its deviation from the linear structure assumed in most algorithms. By collecting and analyzing such data:
- Improved OCR Accuracy: AI models learn to detect, segment, and process curved text accurately, enabling better recognition in real-world scenarios such as logos, product labels, and signs.
- Enhanced Image-to-Text Translation: Training MT systems with curved text improves their ability to translate text from images in various orientations and formats.
- Use Cases: This data is invaluable for industries such as e-commerce and retail, where product packaging often contains curved text.
Data Detectable: Specific Context Training for MT and AI
What It Involves: This category includes capturing specific types of documents or text, such as invoices, menus, receipts, and pages in books or magazines.
AI Challenges and Benefits: Data-detectable tasks focus on context-specific training, which helps AI systems become more adept at understanding and interpreting specialized formats:
- Structured Data Recognition: By analyzing invoices and receipts, AI systems learn to identify and extract structured data (e.g., item descriptions, prices, and totals).
- Contextual Understanding: Training on menus or magazine pages enables AI to discern semantic structures such as headers, footnotes, or highlighted text.
- Tailored MT Applications: Machine translation engines become more context-aware, improving accuracy for industry-specific applications like financial documents or culinary translations.
- Use Cases: These datasets are crucial for business applications like expense tracking apps, document digitization, and specialized MT services.
Printed Text: Enhancing OCR and Translation Consistency
What It Involves: Printed text refers to text that appears in standard, machine-generated fonts and layouts, commonly found in books, magazines, posters, or signage.
AI Challenges and Benefits: While printed text is relatively straightforward for OCR systems to process, it remains a cornerstone of AI training:
- Baseline Training Data: Printed text serves as a foundational dataset for training models to handle clear and unambiguous text.
- Translation Accuracy: Consistent printed text in multiple languages allows MT engines to develop accurate language pairs, especially for less common dialects or specialized fields.
- Improved Context Detection: AI systems become better at distinguishing printed text from surrounding visual elements.
- Use Cases: Industries like publishing, advertising, and education rely heavily on printed text recognition and translation.
Handwritten Text: Pushing AI to Its Limits
What It Involves: Handwritten text includes notes, signatures, and annotations, often with varying levels of legibility and formatting.
AI Challenges and Benefits: Handwritten text presents unique challenges for AI systems due to the variability in individual writing styles and the absence of standardization:
- Advanced OCR Capabilities: Training with handwritten text pushes OCR systems to recognize diverse handwriting styles, even in noisy or low-quality images.
- Inclusive MT Applications: By enabling the translation of handwritten notes or historical documents, AI systems become more versatile and inclusive.
- Semantic Understanding: Handwritten text often contains informal language, which helps AI models understand colloquialisms and regional expressions.
- Use Cases: Applications include digitizing historical archives, processing handwritten forms, and enabling real-time translation of handwritten notes.
Technical Considerations: Device and Format Impact
Flitto’s choice of using iPhones and iPads for data collection, along with .HEIC and .MOV formats, reflects the importance of modern technology and data fidelity in AI training:
- Device Consistency: Using standardized devices ensures high-quality and consistent data collection, reducing variability that could affect model training.
- .HEIC and .MOV Formats: These formats offer superior image and video quality while maintaining file compression, enabling AI models to learn from detailed datasets without excessive storage demands.
How These Tasks Drive Innovation in AI and MT
By tackling diverse challenges, from curved and handwritten text to structured and printed formats, these live text collection tasks are essential for:
- Building Robust AI Models: Diverse datasets ensure AI systems can handle real-world scenarios, including complex geometries and context-specific text.
- Improving MT Accuracy: Exposure to varied text types enhances the linguistic and contextual understanding of MT engines.
- Enabling New Applications: From document digitization to real-time language translation, these tasks unlock innovative applications across industries.
Conclusion
The live text collection tasks outlined by Flitto highlight the intricate and essential work required to train AI and MT engines. By collecting data in diverse categories—curved, data-detectable, printed, and handwritten—companies can develop models that are not only accurate but also versatile. This investment in high-quality datasets paves the way for AI systems that meet the complex demands of a multilingual, digital world.
