By default, PDFs are seldom editable, except by the author. Most users do not have access to tools that would make a PDF editable. Alongside this, a common problem with working with PDFs is the issue of embedded fonts. The text in a PDF might often not be selectable. The problem is that the PDF might never have been text in the first place and might be the photo of a physical page converted to a PDF. The same problem one has to face while extracting data from images, as text in images are not selectable.
So, how does one tackle these issues?
In this article, we discuss how you can extract text from scanned/non-scanned pdf and images.
Let's get right into it.
OCR technology helps scan a document, regardless of whether it is made of text or images, for signs of text. It uses pattern recognition algorithms to recognize whether any part of a document might be an alphabet, number, or character. Once this recognition has been made, the OCR extractor converts this image into text on the document itself or extracts this text from the document to a separate environment. An OCR extractor is an essential piece of technology in multiple domains and applications.
In the absence of OCR extractors, all extraction of data from scanned documents has to be done manually. If your data is available in PDF format, you would need to replicate the same data on an excel sheet before you can analyze it. As you can imagine, this manual data entry is immensely time-consuming and prone to all kinds of manual errors. Often senior management would not have time for manual data processing, so they would have to hire someone to do it or outsource the whole process. On top of that, data cannot be tracked in real-time.
The OCR extractor is a one-stop solution to all these issues. A well trained OCR extractor can extract all the required data in a matter of seconds, with minimal error.
Even if you have an OCR extractor, often they come with a few limitations. Here are just some of the challenges with OCR extractor you might encounter:-
If the document that your OCR extractor is scanning was initially made as a text document, the OCR extractor will likely have an easy task on its hands since the characters will be legible. However, if the document was never text and is an image converted to a PDF, most OCR applications would find it difficult to extract data.
If you are extracting data from a PDF, not all OCR extractors will do a great job. Intuitively, OCR extractors have a tendency to treat horizontally aligned text as a line. As a result, it can have significant difficulties in recognizing tables, which are blocks of individual pieces of text. This can be made eve more difficult if the document contains nested tables - a table within a table.
At Docsumo, we’ve designed a special free tool just to overcome this limitation. With Docsumo’s free table extractor tool, you can extract tables from any scanned and non-scanned PDF document along with images. Go ahead and see for yourself.
The clarity of the image is also a major factor in the performance of the OCR extractor. Only an OCR extractor that has been well trained on a host of different types of images will be able to extract text from images taken in different types of lighting.
Optical Character Recognition (OCR) identifies patterns of light and dark in documents which make up letters, characters, and symbols. While early OCR systems were designed to work with limited fonts, modern intelligent OCR technology is capable of recognizing multiple fonts in documents, handwritten notes, and cursive texts.
How OCR technology works is users first upload scanned images of their documents onto systems. The technology recognizes texts and line items in those documents character by character, carefully going through entire documents. Once the OCR algorithms read data, they extract, and convert documents into editable-text. Users can choose to export their documents as a PDF, JSON, CSV, Excel spreadsheets, or convert into various file formats.
Modern OCR works by using feature detection instead of pattern recognition where individual components of characters, letters, and symbols are analyzed instead of detecting generic fonts. For example, a rule that specifies a program to detect A as two-angled strokes making a pointed end at the top and having a horizontal line crossing in between them - no matter what type of font or style A is written in, the program can detect it.
Handwriting recognition is an exclusive feature of intelligent OCR where programs can read data from comb fields in documents and use touchscreen feature recognition where the software can detect users writing characters line by line and recognize specific features of handwriting styles, thus making it easier to extract texts after the initial reads. Everyday OCR is used for scanning machine-printed texts, handwritten documents, and characters from photo-on-photo images.
Sophisticated OCR solutions are also capable of layout analysis where programs go beyond basic text recognition and can scan tables, layouts, columns, and a variety of data types on documents.
One important factor to consider is that even though OCR can yield a 95% to 99.5% data accuracy, it is nowhere near perfect and requires human proofreading to a certain degree after automated data extraction. Intelligent OCR or ICR takes a different turn as AI models get better at recognizing a variety of fonts and handwriting styles from scanned images, PDFs, and documents, which means the number of human reviews needed becomes lesser as more data is fed to systems.
Some OCR programs can provide error-correction features and support for converting extracted data into multiple languages which is helpful to users. OCR technology has been used since the early 1920s and for the best solutions to work optimally, it is important for users to obtain clear images of scanned documents. This is to help the API capture accurate formatting and streamline the data extraction process.
Businesses as well as individual users require an OCR extractor that overcomes these problems and helps them extract data faster and with better accuracy. Docsumo’s Free OCR scanner is a free and well trained tool to extract data from any document. Try it today and see for yourself!
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.