A PDF file can have text stored as a content stream or it could simply be a scanned image converted to the PDF format. Data extraction from the first kind of PDF document is relatively easier but for the second kind of pdf files and images, accurate data extraction remains a challenge. Extracting data from scanned/non-scanned pdf files and images have been made possible by Zonal OCR.
We'll discuss what Zonal OCR is and how it works in this article.
So, let's jump right into it!
Zonal OCR can be considered the second generation of Optical Character Recognition (OCR) technology. Zonal OCR or template OCR can identify text located in specific areas(zones) on a document or an image. It is used when specific parts of a document need to be extracted zonally.
Traditional OCR extracts data from all fields in documents without discriminating or being specific about the values. Tables, columns, graphical elements, scanned images, etc., all data fields are read when processing paper documents with these solutions. Zonal OCR focuses on extracting specific areas of documents and goes beyond traditional optical character recognition by distinguishing the fields it extracts from the rest. The software algorithm doesn’t just convert scanned images into text, it understands the structure and hierarchy of your documents. And the extracted data is stored in structured databases, with custom data extractions being possible for different document layouts. Modern OCR uses pre-defined templates for data extraction but Zonal OCR uses intelligent analysis for recognizing characters and various data fields.
Zonal OCR can be configured into document scanning software to extract specific zones or data fields documents. The OCR software searches for index numbers on pages and creates zones from where the data is extracted. Dynamic forms of Zonal OCR can intelligently reorganize documents and let users use regular expressions to define complex search parameters for customizing data extraction.
Regular expressions are used commonly in Python and Perl for various document parsing applications. Users can set up scanning templates for bulk processing documents by defining zones for different documents and full zonal OCR systems can also be used for extracting meaningful phrases, words, and line items from reports. Some platforms feature a zone designer which is embedded with the document extraction interface and these do not require installing any third-party software for using them. The technology is mainly used for automating data extraction from documents and companies can extract data for sharing it with other staff members in secure and cost-effective ways.
The way Zonal OCR works is that it creates zones in documents and sets margins for full pages. All the data is extracted from within these boundaries and anything outside is left out. Any characters that are partially entered in data fields cannot be read and the program displays these areas as error messages. Creating “smart zones,” using regular expressions, can enable users to optimize data extraction, achieve greater accuracy, and allow users to set formatting rules for advanced document processing post data extraction.
If you have a set of data in PDF format that needs to be extracted in key-value pairs or table format, Zonal OCR is the answer. It can trace different sections in a document and extract text.
Let’s see how Zonal OCR works for invoices.
An invoice contains various fields, such as:-
A well trained Zonal OCR model can identify all of this data separately, extract, and store in a structured database.
Other use-cases of Zonal OCR technology in different industries:-
Docsumo uses advanced AI models to extract data according to different document layouts. Docsumo creates APIs for customizing data extraction and starts with document annotation where users can define ‘zones,’ and annotate fields in the initial document.
Users can create key-value pairs to define fields for data annotation and let the API know which areas they are interested in. The API carefully scans through these fields and extracts all line items associated with them. Docsumo is also capable of reading texts from scanned images, PDF documents, logos, graphics, barcodes, and other visual elements present in documents.
Once the API does key-value pair extraction, it requests the user to review the changes. If the user is happy with them, he/she approves and the API saves the workflow for further document processing.
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.