What is Document Parsing? Let’s Understand.
A company having a huge set of documents has directed all scans and incoming documents to a secured Google Drive account. Imagine a system that can automatically identify the customer orders, invoices, airway bills, delivery instructions, contracts of costumers, extracts the data from these 12,000 daily documents, and posts to the company’s legacy system without any manual intervention by any employee.
Now imagine an insurance company processing claims provided by hospitals is fully automated as the data from each bill uploaded on their website is automatically extracted and posted to the company’s SAP system for the data extraction.
What is OCR?
OCR(Optical Character Recognition) used to convert text and data present in images to machine-encoded format for digital purposes. In our case, the images are digital documents. These are the general methods that are involved in an OCR.
- Image pre-processing: This step includes techniques like image de-skewing, noise removal from the text, binarization of digital image, line detection of text, character segmentation from words, and scaling them.
- Character classification: Machine learning and their algorithms are used here to classify and identify a character based on the training set and the model provided by them.
As per the performance of the OCR based solutions available for such as Identity Card images. These kinds of solutions included proprietary and open-source solutions. The dataset had a good mix of high, medium, low-quality images based on the sharpness in image, noise on text, exposure on it, and size of the image available.
How does the OCR extract text from images?
The system fetches the details from the ID image using the following steps.
- Extract text which is in raw form from the document using OCR.
- Validate the document or ID based on raw text present.
- Parse the relevant information present from raw text using document parser.
What is Document Parsing?
Every standard ID or document has a defined format present in it. The document must have a title, the field of headings, the field contains formats, positioning of the photo, positioning of barcode, document format, and it goes on. Developing regex-based rules to filter relevant text from the document based on its type. These rules were specific to a document type as most documents differ in format.
Steps used to parse fields are as follows
- Remove noise from the text field if present.
- Find the field and the heading line numbers on it.
- Process field and their values based on heading line numbers.
The complete document and parsing process can be found here.
The average response time is of 7 secs for the automated details filling system.
What’s the advantage of OCR?
Once a written page is in this machine-readable text form, you’ll do all types of stuff you could not do before. You’ll search through it by keyword, edit it with an application program, incorporate it into an internet page, compress it into a ZIP file and store it in a lot of less house, send it by email, and everyone sort of alternative neat things.
Machine-readable text may also be decoded by screen readers, tools that use speech synthesizers to browse out the words on a screen thus blind and visually impaired individuals will perceive them.
Use cases of OCR and Document Parsing
KYC and Customer Onboarding
There square measure several establishments that require client documents for the onboarding method. The OCR Solution will scan the KYC or alternative documents to capture the client data or information and feed them into the system while not human involvement needed. Several organizations like financial institutions, hospitals, schools can reduce their resource burden.
Hand-written Physical Forms
Banks, financial institutions, and lots of alternative industries use physical forms stuffed with written texts in their daily operations. Extracting and saving these written texts in digital format could be a tedious manual method. OCR tools can easily scan and extract the data from the physical form and save them in the database.
Invoices, Bills, etc.
Organizations receive invoices, bills, and other documents in either physical form or PDF. Organizations are building bots that can be trained with these documents and the bot can extract data automatically from those physical documents.
A regular loan approval process, humans deal with multiple document validation, credit check, etc. With RPA and OCR based tools,companies are building bots that can read, understand, and validate documents provided by the customer and apply approval logic specified by the bank to approve or disapprove loan application.
First, you would like to urge the simplest attainable output signal of your existing document. Sometimes you’re just stuck with an old typewritten script, but you may be able to improve the print quality by photocopying. the standard of the initial output signal makes a large distinction to the accuracy of the OCR method. Dirty marks, folds, low stains, inkblots, and the other stray marks can all cut back the probability of correct letter and word recognition.
You run the output signal through your optical scanner. Sheet-feed scanners square measure higher for OCR than flatbed scanners as a result of you’ll scan pages one once another. most up-to-date OCR programs can scan every page, acknowledge the text on that, then scan the succeeding page mechanically. If you are employing a flatbed scanner, you will have to insert the pages one at a time by hand. If you’re a fairly smart camera, you will be ready to produce pictures of your pages by taking photos. you’ll likely use a macro (close-up) focus setting to urge extremely sharp letters that square measure clear enough for correct OCR.
Basic error correction
Some programs give you the opportunity to review and correct each page in turn: they instantly process the entire page and then use a built-in spellchecker to highlight any apparently misspelled words that may indicate a misrecognition, so you can automatically correct the mistake. You can usually switch off this feature if you want to if you have many pages to scan and you don’t want to check them all as you’re going along. Sophisticated OCR programs have extra error-checking features to help you spot mistakes.
How does handwriting recognition work?
Recognizing the characters that makeup showing neatness laser-printed pc text is comparatively simple compared to cryptography someone’s scribbled handwriting. that is the reasonably simple-but-tricky, everyday drawback wherever human brains beat clever computers hands-down: we are able to all create a rough stab at the idea the message hidden in even the worst human writing. How? we have a tendency to use a mix of automatic pattern recognition, feature extraction, and absolutely crucially knowledge concerning the author and also the that means of what is being written.
What benefits does OCR bring to you?
With OCR, the recognized document appearance similar to the initial. Advanced, powerful OCR software system permits you to save lots of plenty of your time and energy once making, processing, and repurposing numerous documents. With OCR, you’ll scan paper documents for more redaction and sharing along with your colleagues and partners. you’ll extract quotes from books and magazines and use them for making your course studies and papers while not the necessity for retyping.
With a camera and OCR, you’ll capture text outdoors from banners, posters, and timetables then use the captured data for your functions. within the same manner, you’ll capture data from paper documents and books, for instance, if there’s no scanner shut at hand otherwise you cannot use it. additionally, you’ll use the OCR software system for making searchable PDF archives.
The entire method of information conversion from the initial paper document, image, or PDF takes but a second, and also the final recognized document appearance similar to the initial.