What is regional OCR?
In automated data retrieval applications, one of the most useful developments in optical character recognition (OCR) technology has been regional OCR, also known as Template OCR and Zone OCR technologies. Zonal OCR is useful when certain parts of a document need to be primarily or “zonal” unpacked.
Table of contents
How is regional OCR different from regular OCR?
Regular OCR extracts all information from documents into easily accessible and processable digital data. All substances in the parent document shall be distinguished without distinction by meaning or significance. Often, such data extraction involves further manual extraction of relevant data from the data collected inextricably from the original document.
Zonal OCR, on the other hand, extracts only important and specified data fields from a scanned document and stores the data in a structured database for further automation or processing.
How does Zonal OCR software work?
Regional OCR software is trained to recognize the structure and hierarchy of a document using code or APIs. The OCR engine then divides the document into zones that can correspond to a specific field. These zones are defined by designing appropriate OCR models. These zones are usually location-based, as shown in the following figure, where the user simply draws a square around the data to be decompressed. After reading the entire page as a whole, the texts in the zones are identified and decompressed as specified in the template. Zone OCR can be programmed to ignore graphical elements that do not need to be read, and this reduces the amount of data to be parsed to extract the required data. This improves data retrieval speed and OCR engine accuracy.
The Zonal OCR system is trained by defining where certain data fields can be found within a document. OpenCV, Tesseract, and Python are some regional OCR systems that can be trained to select specific fields from a scanned document.
Different regional OCR software work differently – some require zones to be in the same place in each scanned document, while more advanced tools can be taught to search for zones in different parts of the page.
Benefits of regional OCR
- Regional OCRs allow relevant information to be captured from paper, forms, and electronic documents that can be used directly in the next step of the document automated business process due to the lack of a small number of people. This “non-contact handling” eliminates paper-centric processes and delivers better performance, scalability and agility.
- It avoids unnecessary data.
- Zone-specific data extraction allows easy access to data for the entire team or even the company. This visibility can improve productivity and reduce repetitive and wasteful hassles.
- Extracting zone data can save valuable time that would otherwise be wasted on manual data entry. By McKinsey Digital, CEOs spend nearly 20 percent of their time on automated work, such as data entry, and a 20 percent savings on time could lead to proportionate savings in the bank; In companies, time is, after all, money.
- Zonal OCR software can extract metadata from documents such as names, dates, and invoice numbers, allowing for better data organization and management.
- Advanced regional OCR software can extract predefined data into a custom layout, allowing for easy tracking and easy eyeball scanning for trends and problems.
Regional OCR applications?
Zone OCR software can be used to gather useful information from all types of documents when properly trained. Some areas that benefit from regional OCR are:
Digitization of an invoice: An invoice / invoice is made from different fields, including name, address, dates, products, costs, etc., which are located in different places. A well-trained Zonal OCR algorithm can decompress all of this data separately and store it in a structured database.
Digitization of a purchase order: Like an invoice, a purchase order (and receipt) also contains useful fields that must be stored in the company’s central database. Zonal OCR can help you track purchase orders and receipts.
Digitization of an identity card: Documents such as ID cards are sent continuously for various processes. Manual ID verification and data entry is time consuming and prone to errors. Regional OCR can speed up the data entry process in applications that require the use of ID cards and similar other documents.
Text recognition in images and objects: Capturing a mileage or traffic sign or license plate of a speeding vehicle requires advanced regional OCR software that can extract text from blurry or fast-moving images.
Other applications for regional OCR tools include account statement processing, usage protocol processing, customer database maintenance, invoice processing, and more.
Disadvantages of regional OCR
- Less advanced regional OCRs may fail to extract data from semi-structured documents where the fields to be extracted are not in the same position in all documents.
- Regional OCRs are not able to decode text from complex data fields, such as multi-line mail addresses.
- Regional OCR files also struggle to extract consecutive data fields (e.g., continuing product numbers on the same invoice or receipt).
More advanced artificial intelligence-based OCR tools such as Nanonets that use machine learning techniques can overcome some of these problems.
AI-based OCR with Nanonets technology
Nanonets is OCR software that leverages artificial intelligence and ML capabilities by automatically leveraging unstructured / structured data from PDF documents, images, and scanned files. Unlike traditional OCR solutions, Nanonets does not require separate rules and templates for each new document type.
Relying on cognitive intelligence based on artificial intelligence, Nanonets is able to handle semi-structured and even invisible document types while improving over time. Nanonets algorithm and OCR models are constantly learning. They can be trained or re-trained multiple times and are highly customizable. You can also customize the output just to extract certain tables or records of interest.
The software provides an excellent API and documentation for developers, but it is also ideal for organizations that do not have an internal developer team.
It’s fast, accurate, easy to use, allows users to build custom OCR models from scratch, and has great Zapier integrations. Digitize documents, extract tables or data fields, and integrate into your everyday applications through APIs in a simple, intuitive interface.
See these inspiring customer success stories highlights how Nanonets helped companies grow faster and more productively.
The advantages of using Nanonets over other automated OCR software far outweigh the cost savings, accuracy, and scale. Nanonets also offers unique advantages that put it far ahead of the competition:
- A truly uncoded tool
- No post-treatment is required
- Works with custom data
- Easily handles data constraints
- Works in languages other than English or multiple languages
- Continuous learning
- Unlimited customization