Similarly, with accounting workflows, an OCR system can capture and file receipts to remove the need for manual data entry. Engineering document management often relies on OCR to digitize old drawings before creating a searchable archive that makes it easy to find information about a facility. Modern OCR solutions solve a range of challenges across different industries. Image Source: What Are the Common Applications for OCR Scanning? Grammar – Detecting the language and probable words is possible by identifying verbs or nouns that commonly go together (with the Levenshtein Distance algorithm often applied).Error correction – Using near neighbor analysis improves accuracy by setting up rules for frequently used language. You can also improve the accuracy of the OCR scan output by: Lexicons can range from all words in a particular language or a shortened list of permitted words based on a specific document type. OCR systems use a library of allowable words (called a lexicon) to limit the results from a scan to a particular character. What Is OCR Post-Processing?ĭifferent post-processing techniques are available to increase the accuracy of an OCR scanner’s output. The OCR software will convert each pixel to a binary value and runs different calculations to identify the most likely character. By matching pixels with pattern recognition or line/stroke evaluation, OCR scanners can recognize probable characters. What Is OCR Feature Extraction?Īfter preprocessing, OCR software begins the feature extraction phase. Preprocessing is essential to extract meaningful text from documents, especially when OCR scanning older paper files with poor image quality. Normalization – Corrects the aspect ratio and scale of the document into standard sizes.Segmentation – Divides and links different image artifacts (or single characters) into pieces of text.Script recognition – Used in documents with multiple languages to transform the recognition parameters at the word level.Zoning – Helps to identify captions, columns, and paragraphs as blocks of text in multi-column and tabulated documents.Line removal – Removes non-glyph boxes and cleans out any lines on the document.Binarization – Creates a black and white image of the file to easily distinguish between the characters and the background.De-speckle – To smooth edges and remove positive/negative spots from the document, OCR software uses a de-speckle algorithm.
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