An area of computer vision , OCR processes images of text and converts that text into machine-readable forms. In other words, it takes handwritten or typed text within physical documents and converts them into digital formats. In the s, many business owners used OCR, sometimes called text recognition, to convert physical documents into digital files.
Since then, the quality of OCR technology has improved, but demand has increased for broader usability. Before the invention of OCR, converting physical text to digital was a manual effort: a person would have to retype each document, a time-consuming task prone to mistakes.
With OCR, the conversion happens quickly and with greater fidelity to the original content. Once OCR converts a hard copy into its digital form, viewers can edit, format, and search the document.
They can also send it easily via email, include it in a website, and store it in compressed files. Naturally, this eliminates the need for physical storage space, a cost-savings for businesses that heavily rely on documentation, such as mortgage brokers or legal firms. AI can better interpret handwriting as well, opening up opportunities for digitizing a wider range of documents.
Handwriting still presents a challenge to AI due to the uniqueness of each individual, but with more handwriting training data, machines are gaining greater ability on that front as well. This document comprehension capability helps businesses analyze numerous documents without committing human labor to the task. Reducing tedious administrative work can be critical to maximizing employee engagement and reducing turnover.
Researchers expect demand in AI-powered OCR to continue as these tools become more efficient and cost-effective. An OCR system features a combination of hardware and software. Think of this in context of postal and mail sorting services — OCR is core to their ability to operate quickly in processing destination and return addresses to sort mail faster and more effectively.
The system does this in three steps:. In step one, the hardware usually an optical scanner processes the physical form of the document into an image — such as an image of an envelope. The goal of this step is for the machine to be accurate in its rendition, but also to remove any unwanted distortions. Turn read-only files into editable text OCR solutions pull read-only text from files such as PDFs so you can edit it, use it in other documents, and search for it.
Create audible files You can save the time you spend reading long or complex documents by turning them into a natural-sounding audio file.
So you can listen to the document on your commute or at the gym, making you more productive. This also provides blind and visually-impaired people access to written text. Translate foreign languages There are some OCR solutions that can convert documents from more than foreign languages. Manage forms and questionnaires Managing manually-completed forms and questionnaires used to take hours upon hours of time and effort. OCR lets you scan the documents instantly, turning the information into searchable text so you can extract insights or take action sooner.
Achieve faster, more accurate data entry By using OCR and digitalising data sources, you can automate data entry and avoid the need to rekey information into systems. This can save time and eliminate the errors that can creep in when people enter data manually. Request a quote Tagged in: Blog , Optical character recognition.
While these work great on simple OCR datasets like easily distinguishable printed data and handwritten MNIST data, they miss out on many features, making them fail when working on complex datasets. Deep learning-based methods can efficiently extract a large number of features, making them superior to their machine learning counterparts. Algorithms that combine Vision and NLP-based approaches have been particularly successful in providing superior results for text recognition and detection in the wild.
Furthermore, these methods provide an end-to-end detection pipeline that frees them from long-drawn pre-processing steps. Generally, OCR methods include vision-based approaches used to extract textual regions and predict bounding box coordinates for the same.
The bounding box data and image features are then passed onto Language Processing algorithms that use RNNs, LSTMs, and Transformers to decode the feature-based information into textual data. Deep learning-based OCR algorithms have two stages—the region proposal stage and the language processing stage.
The task of the network here is similar to the Region Proposal Network in object detection algorithms like Fast-RCNN, where possible regions of interest are marked and extracted. These regions are used as attention maps and fed to language processing algorithms along with features extracted from the image. Fully CNN-based algorithms that recognize characters directly without going through this step have been successfully explored in recent works and are especially useful to detect text that has limited temporal information to convey, like signboards or vehicle registration plates.
State-of-the-art neural networks have become exceptionally good at spotting text in documents and images, even if it is slanted, rotated, or skewed. We've added a public Text Scanner model to our Neural Networks page to help you detect and read text in your images automatically. Before we can start effortlessly pulling text from images and documents, we'll need to get three quick setup steps out of the way:.
A person who is blind or visually impaired can access the scanned text by using adaptive technology devices that magnify the computer screen or provide speech or braille output. Current generation OCR systems provide very good accuracy and formatting capabilities at prices that are up to ten times lower than a few years ago.
This does not include the personal computer and screen access equipment. Allows user to scan and read aloud magazines, books, or receipts. Easy to operate with distinctively shaped buttons. Provides option to save or post interesting articles for later reference or archive single and multiple page documents and access them when needed. Allows user to scan and read aloud magazines, books, or receipts while magnifying them on a monitor. User may change the appearance of any printed text to his or her preference with a press of a button.
Does not include batteries. Has a simple one-button, spam-free, email system. May be used for scanning a printed page, pill bottle, recipe card, or newspaper. Motion detector automatically senses when a new page is placed under the camera. Offers fully readable letter size with an option for column recognition and allows documents to be stored for later use.
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