“A well-known technology, known as optical character recognition (OCR), is used to identify text within images, such as scanned documents and photographs. OCR technology is used to transform any document containing written text into machine-readable data, whether it is typed, handwritten, or printed. Records and images can be read and analysed using OCR software. Quite impressively, it also translates the gathered data into a readable language for computers.
While trying to digitise old newspapers in the early 1990s, OCR was introduced but gained popularity in the 1920s. As you may guess, technology has advanced significantly since then. Most characters and fonts can now be recognised with high precision.
As one example, OCR can extract data from images of tables, such as those seen in scanned PDFs. Receipts, invoices, contracts, and more all follow the same format and use our table OCR functionality. Using the receipt scanning and/or table recognition options on the main page is highly recommended for all of these documents. The OCR API is the same as turning on OCR mode on the table.
Benefits of Table OCR
As more programs, software, and internet platforms are developed, more data is collected. Improving data management and access requires practical information extraction techniques. Consider extracting tabular data from a large number of documents for further processing. If you want, you can print the data or import it into an Excel spreadsheet.
Fortunately, table OCR software detects tables and extracts all tabular data from a page at once. This saves a lot of time and effort. The table extraction method can also be used for personal use. We occasionally take a photo of a document with our phone and upload it to our computer. Alternatively, documents can be captured directly and stored in editable formats in our custom templates. Here are some examples of everyday table extraction.
Excel spreadsheets and offline forms are used in a variety of sectors. The pages and paperwork can be tough to search through at times. These tables take a long time to hand enter, and there is a considerable risk of data entry errors. Table extraction is a better way to solve business problems than other methods.
The Past of OCR
OCR has its origins in the telegraph system. On the eve of the First World War, Emanuel Goldberg, a scientist, devised a machine to read texts and turn them into telegraph code. The first electronic document retrieval system was invented by him in the 1920s.
The microfilming of financial information at this time was a good idea, but it was nearly impossible to locate specific records quickly. Using a movie projector and a photoelectric cell, Goldberg solved this problem. He began the process of automating record-keeping by repurposing already existing technologies. IBM later purchased the patent for his "Statistical Machine" in the US.
Since then, businesses worldwide have relied on OCR technology to reduce the costs of converting and extracting data from paper documents. OCR technology has increased since then.
In earlier iterations of OCR, only a single typeface could be recognised, and each character had to be taught using pictures. The "Omni-font OCR," developed by Ray Kurzweil in the 1970s, could read text printed in almost any typeface. In the early 2000s, OCR became a cloud-based service, making it available to desktop and mobile applications.
Thanks to a wide range of OCR service providers (typically via APIs), most characters and fonts can be recognised to a high degree of accuracy. Even as technology improves, there is always room for human error. As a result, the organisation will be forced to spend time and money validating information before sharing it with the rest of the organisation. This is in addition to the environmental and economic costs associated with continuing to use paper.
It transported paper documents like bank statements and business cards when it was first developed. OCR software was used to convert any image-based document into an editable PDF. These tools have made document management and online storage more accessible than ever before. Numerous OCR programmes and apps are available for desktops and mobile devices. The pricing of a given software or service depends on the specific combination of characteristics that make it what it is. If you're frequently surrounded by paper, there's a strong chance we have an OCR app for you.
When an electronic software document cannot be generated, OCR will remain a vital tool.
The future of OCR
Aside from electronic data interchanges (EDI) and invoice portals, OCR has long been the only method of transforming paper invoices into computer-processable data, as can be shown in the example of financial systems.
After years of seeing and matching, OCR is finally evolving. Deep learning has propelled it into a new phase where it first detects scanned text and then attempts to make sense of what it has read. To stay ahead of the competition, you'll need software that can extract as much information as possible while also providing the highest level of insight quality. As a result, there's room for many different businesses to thrive because each industry has its own unique document formats, structures, and rules.
Licenses and payment terms for standard OCR services should be reviewed. In addition, they can test out free OCR services like Amazon's Textract or Google's Tesseract to see if the current advancements in OCR correspond with their business objectives. The reach of RPA and AI suppliers that positively impact the industry must also be considered.
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