![]() Pattern matchingĪ character picture, or glyph, is isolated throughout the pattern matching process and compared to a previously recorded glyph. The two main types of OCR algorithms for text recognition are pattern matching and feature extraction. The OCR software first cleans the image and removes errors to prepare its data for reading. The OCR softwares identify the scanned image and classify the light areas as background and the dark areas as text. In this process, a scanner reads documents and converts them to binary data. The OCR engine works by using the following steps: Image acquisition OCR technology solves the problem by converting text images into text data that can be analyzed by other business software. Text in images cannot be processed by word processing tool in the same way as text documents. Moreover, digitizing such documentation content creates images with the text hidden within it. Different documents like paper forms, invoices, scanned legal documents, table extraction, handwritten texts and printed text or contracts are all part of business processes. In this modern era, most business workflows involve receiving information from print media. However, you can use OCR solutions to convert the image file into a text document with its contents stored as text data. The data in the image file cannot be edited, searched for, or counted using a text editor. For example, if you scan a form, invoices or a receipt, your computer saves the scan as an image file. The procedure used to transform an image of text into a machine-readable text format is known as Optical Character Recognition (OCR). Published OctoComparison between IronOCR and AWS Textract OCR What is OCR? OcrResult.Choice Choices = character.Choices Output alternative symbols choices and their probability. ![]() Int CharacterNumber = character.CharacterNumber ĪnyBitmap CharacterImage = character.ToBitmap(ocrInput) ĭouble CharacterOcrAccuracy = character.Confidence Pages -> Paragraphs -> Lines -> Words -> Characters OcrResult.TextFlow paragrapthText_direction = paragraph.TextDirection ĪnyBitmap LineImage = line.ToBitmap(ocrInput) ĭouble LineOcrAccuracy = line.Confidence ĪnyBitmap WordImage = word.ToBitmap(ocrInput) ĭouble WordOcrAccuracy = word.Confidence įoreach (var character in word.Characters) Int ParagraphNumber = paragraph.ParagraphNumber ĪnyBitmap ParagraphImage = paragraph.ToBitmap(ocrInput) ĭouble ParagraphOcrAccuracy = paragraph.Confidence OcrResult.Barcode Barcodes = page.Barcodes ĪnyBitmap PageImage = page.ToBitmap(ocrInput) ĭouble PageRotation = page.Rotation // angular correction in degrees from OcrInput.Deskew()įoreach (var paragraph in page.Paragraphs) OcrResult ocrResult = ocrTesseract.Read(ocrInput) OcrInput.AddMultiFrameTiff("example.tiff") This allows us to explore, export and draw OCR content using other APIs/ Pages, Barcodes, Paragraphs, Lines, Words and Characters ![]() We can delve deep into OCR results as an object model of Var ocrResult = ocrTesseract.Read(ocrInput) Optional: Export modified images so you can view them.įoreach (var page in ocrInput.GetPages()) String codeToRun = out double confidence, ocrTesseract) WIZARD - If you are unsure use the debug-wizard to test all combinations: First load all Note: You don't need all of them most users only need Deskew() and occasionally DeNoise()
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