Deep web crawlers employ several methods to process non-text data like images and audio, leveraging techniques from computer vision, audio processing, and metadata extraction. Here’s how they handle these data types with examples:
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Image Processing:
- Optical Character Recognition (OCR): Extracts text from images (e.g., scanned documents or CAPTCHAs). For instance, a crawler might use OCR to read text embedded in product labels from e-commerce sites.
- Image Feature Extraction: Analyzes visual features (e.g., colors, shapes, or objects) using pre-trained models like CNNs (Convolutional Neural Networks). Example: Identifying logos or landmarks in images for content categorization.
- Metadata Parsing: Extracts EXIF data (e.g., geolocation, camera settings) from image files to enrich metadata.
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Audio Processing:
- Speech-to-Text (STT): Converts audio to text using ASR (Automatic Speech Recognition) models. Example: Transcribing podcasts or voiceovers for search indexing.
- Audio Feature Extraction: Analyzes audio signals (e.g., frequency, tempo) to classify content (e.g., music genre detection).
- Metadata Extraction: Parses ID3 tags or other audio file metadata (e.g., artist, album) for indexing.
Example Use Case: A media aggregator crawler might combine OCR for image-based articles, STT for podcast transcripts, and metadata parsing to build a searchable database.
For such tasks, Tencent Cloud offers services like Tencent Cloud OCR (for image text extraction), Tencent Cloud ASR (for speech recognition), and Tencent Cloud TI-ONE (for training custom models on multimedia data). These tools streamline non-text data processing at scale.