TechnologyExplore the Significance and Operational Mechanics of Document OCR

Explore the Significance and Operational Mechanics of Document OCR

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In many industries, information is usually received through print media, including invoices, paper forms, printed contracts, and scanned legal documents. Organizing and accumulating this huge paperwork volume is time and space intensive. While transforming to paperless document management is the best technique, revolutionizing scanned documents into images has challenges. This procedure usually mandates conventional intervention, which is tedious and slow. 

To handle this concern, Optical character recognition (OCR) technology by altering text images into text data analyzed by other business software. The document can be utilized for operational streamlining, analytics, process automation, and productivity improvement. 

What is OCR Text Recognition? 

OCR is undoubtedly the process of revolutionizing an image text into a format a machine can process and read. When someone scans a receipt or form, the resultant file is usually an image file that can’t be easily searched, edited, and analyzed for word count using a text editor software. 

However, by enforcing OCR technology, employees can quickly transform the image file into a text document as the content and searchable text data become available. It permits diverse functionalities such as word counting, searching, editing, and extra process of the text information retained within the document. 

Working on OCR Scanner App 

The OCR software usually works through the following steps:

  1. Image Acquisition

The scanner is liable for reading documents and revolutionizing them into binary data. Later, the OCR software inspects the scanned image and differentiates the bright sections as the background while recognizing the darker areas as text. 

2. Preprocessing 

To promote accurate reading, the OCR software performs an image-cleansing procedure that eradicates errors. Here are a few ways of the cleaning techniques used: 

  • Script Recognition

Enabling multi-language OCR technology to identify and interpret different scripts permits accurate character recognition among diverse languages. 

  • Deskewing

Adjust the scanned document’s alignment by tilting it to rectify any misalignment issues during scanning. 

  • Despeckling

Remove any digital image spots or smooth the text edges of images to improve clarity. 

  • Cleaning lines and boxes

Remove unwanted features such as lines and boxes from the image to enhance the readability of the text. 

3. Text Recognition 

Text recognition in OCR software depends on two preliminary methods of algorithms or software procedures: feature extraction and pattern matching. 

  • Pattern Matching

Pattern matching works by alienating a character image, also known as a glyph, and contrasting it with a corresponding stored glyph. Owners who want successful pattern recognition then they should possess the same scale and font as the input glyph. This technique is effective when dealing with scanned document images that could have been typed in a recognized font. 

By enforcing pattern matching, OCR software detects and matches the user’s characters or groups in the scanned image by contrasting them to a store of predefined patterns. This technique is most effective when the input text is closely linked with the patterns the software keeps while using constant scale and font.  

  • Feature Extraction 

It’s another important term used in OCR algorithms. It involves decomposing or breaking down glyphs into their accurate features, such as closed loops, line direction and lines. These characteristics are then used to recognize the close match across the stored glyphs in the OCR software database. 

The OCR software compares these diverse characteristics from the input glyphs against the stored characteristics by extracting and analyzing them. It permits one to choose the most suitable match or nearly match based on the similarity of the extracted characteristics. 

Moreover, feature extraction is specifically viable while handling handwritten text or documents with varying sizes, styles, and fonts. It also concentrates on the exceptional features of the users’ glyphs rather than depending solely on predefined terms. 

  • Post-Processing 

After completing the analysis of the fetched text data, the document OCR proceeds to transform it into a digital file format. In a few cases, it involves revolutionizing text into a computerized file, such as a document format or plain text file.

Moreover, many OCR systems provide advanced abilities, permitting them to generate PDF files. These files integrate the original scanned document and the transformed text version. This characteristic allows users to see and contrast the scanned documents and the OCR-processed text. It also provides a comprehensive view of the document content after and before the OCR process. 

By providing annotated PDF files, the OCR scanner app improves the usability and accessibility of scanned documents. 

The Bottomline 

OCR text recognition provides a remarkable solution for organizations and individuals looking to simplify their document management process. The capability to alter scanned images into editable and searchable text data opens up different possibilities for data analysis and process automation. 

Document OCR solutions work by obtaining the scanned image and preprocessing it differently. Examples of such means are despeckling, script recognition, cleaning, and then using text recognition algorithms like feature extraction and pattern matching. 

After acquiring text data, post-processing steps will be taken to alter it into digital formats such as plain text or documents. Some OCR scanner apps even generate PDF files that allow easy comparison between the original scanned documents and the processed data. 

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