What is Zonal OCR?
In automated data extraction applications, one of the most useful developments in Optical Character Recognition (OCR) Technology has been the Zonal OCR, also called Template OCR and Zone OCR. Zonal OCR is useful when specific parts of a document must be preferentially or “zonally” extracted.
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Zonal OCR vs Traditional full OCR
Full OCR extracts all data from documents into accessible and manipulatable digital data. All matter present in the parent document is extracted with no differentiation by relevance or importance. Oftentimes, this kind of data extraction entails further manual extraction of relevant data from the ensemble of information indiscriminately gathered from the original document.
Zonal OCR, creates zones within the documents by adding specific margins on the other hand, and extracts only the important and specified data fields from a scanned document and stores the data in a structured database, for further automation or processing.
Zonal OCR improves the accuracy of data extraction process & allows more control over the formatting & document processing.
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How does Zonal OCR software work?
The zonal OCR software is trained to identify the structure and hierarchy of a document through code or API. The OCR engine then splits the document into zones that could correspond to a particular field. These zones are determined through design of appropriate OCR templates. These zones are usually location-based, as shown in the following figure, in which the user simply draws a square around data that must be extracted. Then, instead of reading the entire page as a single entity, the texts in the zones are identified and extracted as specified in the template. The zone OCR can be programmed to ignore graphical elements that need not be read, and this reduces the amount of information that needs to be parsed in order to pick out the needed data. This enhances data extraction speed and accuracy of the OCR engine.
The Zonal OCR system is trained by defining where specific data fields can be found inside a document. OpenCV, Tesseract, and Python are some zonal OCR systems that can be trained to pick out specific fields from a scanned document.
Different zonal OCR software work differently – some require that zones be in the same location in every scanned document while more advanced tools may be taught to look for the zones in different parts of the page.
Advantages of zonal OCR
- Zonal OCRs allow capture of relevant data from paper documents, forms and e-documents that can be directly used in the next step of the automated business process with need for minimal human intervention. This “touchless processing” removes paper-centric processes and delivers better performance, scalability, and agility.
- It avoids redundancy in data.
- Zonal data extraction enables easy access to data to the entire team, or even company. This visibility can enhance productivity and reduce repetitive and wasteful effort.
- Zonal data extraction can save valuable time otherwise wasted on manual data entry. According to McKinsey Digital, CEOs spend almost 20% of their time on work that could be automated, such as data entry, and a savings of 20% on time could lead to a commensurate savings in the bank; in businesses time is money after all.
- Zonal OCR software can extract metadata from documents such as names, dates, and invoice numbers, which enables better data organization and document management.
- Advanced zonal OCR software can extract predetermined data into a tailored layout, enabling easy tracking and easy eyeball scanning for trends and issues.
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Applications of zonal OCR?
Zone OCR software can be used to extract useful information from any kind of document, when properly trained. Some areas that are benefited by zonal OCR are:
Invoice digitization: An invoice/bill is made of various fields including name, address, dates, products, cost etc. placed at different locations. A well-trained Zonal OCR algorithm can extract all of this data separately and store it as a structured database.
Purchase Order digitization: Much like the invoice, a purchase order (and receipt) also contains useful fields that need to be stored in a central database of a company. Zonal OCR can help with keeping track of purchase orders and receipts.
ID card digitization: Documents such as IDs are constantly submitted for various processes. The process of manual ID validation and data entry is time consuming and prone to errors. Zonal OCR can hasten the data entry process in applications that require use of id cards and such other documents.
Text detection in images and objects: Capturing a meter reading, or a road sign, or the license plate of a speeding vehicle requires advanced zonal OCR software that can pick up text from blurry or fast-moving images.
Other applications of zonal OCR tools include bank statement processing, operating protocol processing, customer database maintenance, bills processing, etc.
Nanonets has interesting use cases and unique customer success stories. Find out how Nanonets can power cognitive data capture for your business.
Challenges of using Zonal OCR
Document Quality
Zonal OCR has issues regarding document quality as it may not perform well with images that are of low resolution or with images that smeared, distorted or faded. These imperfections could easily channel towards poor data extractions which in turn call for high error rates.
Flexible Structure of Document
There is always discrepancy on the layout and format of documents that are usually used. This makes it difficult for zonal OCR to design different structures that fit the zones hence making it difficult to point and extract data from zones that are not much similar to the template.
Handling Complex Formats
In addition, zonal OCR becomes a challenge when the document is filled with tables, graph or a combination of the two or more. It may fail to parse the information effectively from these formats and diagnose or categorise it appropriately.
Scalability Issues
The downside of zonal OCR systems is that, as the quantity of documents rises, it may have scalability problems. In a large scale data management it may become complex to manage and process the documents in effective manner which in turn might cause to slow down the data processing and increased cost.
More advanced AI-based OCR tools such as Nanonets that use machine learning & image processing techniques can overcome some of these issues.
AI based OCR with Nanonets
Nanonets is an OCR software that leverages AI & ML capabilities to automatically extract unstructured/structured data from PDF documents, images and scanned files. Unlike traditional OCR solutions, Nanonets doesn’t require separate rules and templates for each new document type.
Nanonets demo
Relying on AI-driven cognitive intelligence, Nanonets can handle semi-structured and even unseen document types while improving over time. The Nanonets algorithm & OCR models learn continuously. They can be trained or retrained multiple times and are very customizable. You can also customize the output, to only extract specific table or data entries of your interest.
While offering a great API & documentation for developers, the software is also ideal for organizations with no in-house team of developers.
It is fast, accurate, easy to use, allows users to build custom OCR models from scratch and has some neat Zapier integrations. Digitize documents, extract tables or data-fields, and integrate with your everyday apps via APIs in a simple, intuitive interface.
Check out these inspiring customer success stories that showcase how Nanonets helped businesses grow quickly and be more productive.
The benefits of using Nanonets over other automated OCR software go far beyond cost savings, accuracy and scale. Nanonets additionally provides unique benefits that place it far ahead of the competition:
- A truly no-code tool
- No post-processing needed
- Works with custom data
- Easily handles data constraints
- Works with non-English or multiple languages
- Continuous learning
- Infinite customization
Update Oct 2022: this post was originally published in July 2021 and has since been updated.
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