When it comes to PDF documents, it's crucial to understand the difference between two fundamental types of content: raster and vector. Choosing the right format can greatly affect how information is extracted and used, especially with the help of Fonn technology.
Vector Format for Precision
Vector emerges as the preferred format for PDF drawings and specifications due to its compatibility with our technology. This choice ensures the most accurate results in populating information. It's advised to request vector PDFs from the design team, steering clear of raster PDFs or those with a mix of content types.
Identifying Raster Content
Raster files are characterized by a static grid of pixels, lacking distinct lines or characters. Fonn's technology attempts to discern lines and letters based on pixel shapes. To identify raster content:
- Attempt to highlight text; if not possible, it's raster.
- Zoom in; blurriness or pixelation indicates a raster file.
- Verify if the file was scanned; scanned files are raster-based.
Transition to Vector Content
If a raster-based PDF is received, collaboration with the design team is recommended to convert the original content into a vector-based PDF. This transition ensures better compatibility with our solution and enhances the accuracy of text parsing.
Identifying Vector Content
Vector files are based on a mathematical model, creating links between points and displaying sharp line segments. To identify vector content:
- Attempt to highlight text; if successful, it's vector.
- Zoom in; sharpness indicates a vector file.
Optimal Zooming with Vector Content
Vector content allows for nearly infinite zooming without compromising the sharpness of lines and text. This characteristic aligns seamlessly with Fonn's and text parsing technologies, making it easier to identify and extract information accurately.
Recognizing the differences between raster and vector content in PDFs is essential for efficient data handling and accurate document interpretation. Embracing vector formats, understanding identification techniques, and collaborating with design teams contribute to an enhanced workflow and improved results in metadata extraction in Fonn.