How does Tesseract for OCR work?
The Optical Character Recognition (OCR) system converts a wide range of documents into machine-readable texts, allows the extraction of data from images and text documents, and converts them into searchable formats.
In this article, we explore the 9 types of documents that OCR systems can recognize.
So, let's jump right into it:-
More often than not, document quality determines the performance of the OCR system’s data extractions. Text recognition works on predefined algorithms and parameters for pattern and text recognition.
Low-quality pictures with blurry texts, noise, and distorted data tables affect the accuracy of data extractions. In such cases, consider employing techniques such as denoising, deskewing, binarization, and image resizing to enhance the quality of the document.
By using pattern-based matching algorithms, it compares text images character-wise to its internal database. The solution cannot capture handwritten text.
Intelligent character recognition technology uses advanced methods like machine learning to analyze text over many levels, processing the image repeatedly: to read text in the same way as humans. Although it processes images one character at a time, the process yields results within seconds.
Similar to the ICR system, it processes whole word images instead of preprocessing images into characters.
It identifies logos and watermarks in documents.
The algorithm that powers OCR systems can always be modified or upgraded to deal with newer document types using training modules. The software, powered by machine learning algorithms, constantly analyzes new document types and adapts to their layouts. This, in turn, decreases error rates and increases accuracy by up to 99%.
Not all optical character recognition systems are equipped to handle the different document types. Some platforms require different training modules to process documents with custom fields and designs.
They are the most common type of document processed using OCR technology. This category includes books, articles, letters, invoices, receipts, and any other text-based material that has been printed on paper. The document processing system quickly converts these physical documents into editable digital formats, such as Word documents or searchable PDFs, with almost 99.9% accuracy.
In most cases, companies digitize their printed text documents to create a digital archive that is easily searchable and accessible as it is stored in the cloud.
Pattern recognition wildly varies when it comes to business documents, as most organizations use a variety of documents. They include invoices, agreements, contracts, financials, and bank statements, and keeping track of all these documents for multiple vendors and clients can get overwhelming.
An OCR platform not only scans these documents but also stores them in a central repository. It helps employees, auditors, managers, C-suite professionals, and accountants with document retrieval. The data collected from documents can be analyzed for insights to help stakeholders with decision-making.
Organizations in highly regulated sectors require their customers to fill out several forms and applications to adhere to stringent regulatory requirements.
For example, in the banking and lending industries, the customer has to fill out an extensive loan application, which then goes to the loan officers for approval.
Alongside this, businesses are also bogged down by official documentation forms, such as tax and registration forms, among many others, and keeping track of physical paperwork becomes challenging and inefficient.
The OCR software simplifies the workflow by extracting the necessary data and uploading it to the server in a structured format. Respective departments can track the status of relevant documents and archive them for further processing/reference.
One of the standout features of optical character recognition technology is its ability to discern handwriting and extract data accordingly. The data extraction feature works best with block letters but struggles with cursive writing. This is because OCR technology relies on finding uniform patterns, and cursive handwriting is rarely consistent.
Advanced OCR solutions can identify cursive with great accuracy, provided the document contains neat handwriting. But, in most cases, using OCR for such handwritten documents has a high probability of errors.
OCR systems are also capable of extracting the necessary information from passports, driver’s licenses, and other similar ID cards. The system uses the key-value pair technique to identify important data fields and then extracts the text accordingly.
For example, on a driver’s license, address, blood group, DOB, and gender are a few of the key-value pair fields.
Banking and lending businesses use this feature to streamline their onboarding and KYC processes.
There are two major use cases for OCR technology for data extraction in the healthcare industry.
The optical character recognition platform enables hospitals and clinics to record and maintain patient data in a systematic way. The software then automatically formats the documents and transmits them for storage on the cloud. Employees can access patient’s records whenever needed through the centralized hub.
In addition, it helps healthcare organizations stay compliant with the HIPAA regulations that stipulate that organizations must protect the privacy of their patients’ health records.
The OCR software also expedites the claim settlement process after a hospital visit. With less paperwork, the hospitals can discharge the recovering patients quickly and improve the overall customer experience.
Companies also use optical character recognition technology to digitize their guidebooks and walkthrough documents. The digitization of these materials helps create a knowledge database that can be used by customers and employees alike.
Indirectly, the implementation of the software facilitates a better employee onboarding process and improves overall customer satisfaction.
Document processing OCR technology can handle multilingual documents by recognizing and extracting text from different languages. The advanced algorithms identify the language and apply the appropriate model for accurate data extraction.
However, when a document contains multiple languages within a single line or paragraph, the system would struggle to accurately recognize the text due to its contextual limitations.
The document processing technology efficiently processes documents containing tables by accurately identifying fields, columns, checkboxes, and text fields.
However, challenges arise with complex tables featuring merged cells of varying sizes. This hinders accurate recognition of table structures and, in turn, hampers the data extraction process.
The optical character recognition technology extends its capabilities to images within documents, extracting text even from photos containing logos and watermarks, which proves beneficial for redacting sensitive information.
However, complex images pose challenges, especially when text appears in non-standard fonts or gets distorted. In such cases, the platform struggles to achieve accurate text recognition and extraction.
Despite these limitations, OCR systems are a valuable tool for transforming image-based content into searchable and editable text, streamlining document management, and enhancing data accessibility.
Intelligent document processing software, Docsumo, uses an advanced OCR algorithm for accurate data extraction.
Let’s take a closer look at how Docsumo used OCR-based document processing to streaming the workflows for Jones, an insurance provider.
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