DetectAreasMode
DetectAreasMode enumeration
Determines the type of neural network used for areas detection.
public enum DetectAreasMode
Values
| Name | Value | Description |
|---|---|---|
| NONE | 7 | Doesn’t detect paragraphs. Better for a simple one-column document without pictures. |
| DOCUMENT | 8 | Detects paragraphs uses NN model for documents. Better for multicolumn document, document with pictures or with other not text objects. |
| PHOTO | 9 | Detects paragraphs uses NN model for photos. Better for image with a lot of pictures and other not text objects. |
| COMBINE | 3 | Detects paragraphs with text and then uses other NN model to detect areas inside of paragraphs. Better for images with complex structure. |
| TABLE | 4 | Detects tabular structures in the image and extracts text from individual cells. Recommended for scanned spreadsheets, reports, and other table-based documents. |
| CURVED_TEXT | 5 | Automatically straightens curved lines of text in the image, improving recognition accuracy and allowing more text to be recovered and extracted. Requires significant processing power and RAM. |
| TEXT_IN_WILD | 6 | A super-powerful neural network specialized in extracting words from low-quality images such as street photos, license plates, passport photos, meter photos, and photos with noisy backgrounds. |
| LEAN | 0 | Prioritizes speed and reduces resource consumption by omitting support for complex layouts. Suitable only for simple images with a few lines of text without illustrations or formatting. |
| MULTICOLUMN | 1 | Detects large blocks of text formatted in columns. The best choice for multi-column layouts such as book pages, articles, or contracts. |
| UNIVERSAL | 2 | Detects all blocks of text in the image, including sparse and irregular text on photos. A versatile option for most images, except for tables and multi-column layouts. |
Remarks
Used in the RecognitionSettings to specify which type of image you want to recognize.
See Also
- namespace Aspose.OCR
- assembly Aspose.OCR
