Modern OCR software solutions often find it difficult to read the order and face spelling errors while scanning information, and are unable to extract specific regions of interest from files.
OCR software is unable to extract complex entities spanning across multiple lines and pages and suffers from poor quality transcriptions when it fails to analyze the structure of documents correctly. Document layout analysis refers to the process of locating and segmenting information based on the structure of documents and uses visual cues to partition content. We’ll get more into that below.
What is a layout? Put simply, it is the visual design of your document.
A layout can be defined as the collective arrangement of information presented on forms. Tables, cells, images, logos, etc., all these elements together make up a document layout. Every layout is unique and this visual design and information ordering is what’s used for distinguishing different document types.
Examples of document layouts include responsive grid designs, document formatting, page numbers, tabs, company logos, and various graphical/textual elements. The format of an invoice, Acord form, tax receipt, typography, and overall look of a document are the core components of layout designs.
Looking past the visual cues, how the information on documents is ordered and presented also make up a part of document layouts.
When you refer to a document layout, it refers to a single document. Each document layout uses a specific set of parsing rules and when you’re processing multiple documents, document layout analysis becomes a crucial feature for modern OCR solutions.
Here’s why it’s important:
You can use AI and neural networks with object detection to convert physical documents to images for easier reading. When a layout is correctly interpreted, there is a higher chance for data accuracy and precision. You can save these layouts for automated data entry of similar document types.
Probably the biggest benefit of document layout in OCR is the organization of unstructured data and labeling. You don’t have to worry about the algorithm failing to detect the edges of characters, missing details, etc., since neural network creates bounding boxes for consolidating and segmenting information
Traditional OCR solutions can incorrectly read document layouts and cause errors in automated data entry. By incorporating document layout analysis, businesses can save time and prevent the hassle of re-entering data due to incorrect layouts. Machine learning algorithms can classify document types and automatically read data or specific regions of interest based on new structures which previously didn’t exist
It is easier to automate data entry when documents follow a predefined layout or design. When information record-keeping follows a specific format, the data becomes searchable, coherent, and reliable. Making updates to these documents, adding/editing details, managing information, and removing duplicate fields automatically are the end results of good document layout design.
Finally, if you’re looking for an OCR solution that takes advantage of this technology, Docsumo is the right answer. Sign up for a free demo with us, upload your documents, and watch the magic happen.
You’ll never look at document processing the same way!
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.