Intelligent Character Recognition (ICR) is an advanced technology that surpasses Optical Character Recognition (OCR) by accurately extracting handwritten text from image files. By leveraging Machine Learning algorithms and AI, ICR intelligently analyses data from physical documents and forms, enabling the recognition of diverse handwriting styles and fonts, thus enhancing accuracy and interpretation.
ICR plays a crucial role in scanning and extracting detailed information from paper-based documents and securely digitizing the data within a software database. This integration of data with business workflows facilitates streamlined processes and enables analytical reporting. Organizations employing ICR technology can efficiently organize unstructured data and gain real-time insights from these reports.
Furthermore, It boasts a high level of accuracy and serves as a reliable solution for saving time in processing various types of documents.
How Does ICR Work?
Robotic Process Automation (RPA) is a cutting-edge technology that combines Intelligent Character Recognition (ICR) software to execute tasks precisely and without errors. RPA harnesses capabilities such as pattern or speech recognition, computer vision, and AI, enhancing modern ERP suites’ functionalities.
By leveraging RPA and ICR, organizations can generate highly accurate reports and enable humans to extract valuable insights from sophisticated analytics. The operational mechanism of Intelligent Character Recognition (ICR) in relation to document extraction for organizations can be described as follows:
- Upload documents to the ICR scanner.
- Thoroughly read pre-trained APIs or scan the picture that will automatically interpret handwritten text or fonts and take references using a database.
- The software asks the user to upload handwritten signatures for verification.
- After extracting data using key-value pairs and can be automatically structured.
- After proofreading and reviewing for spelling errors, the ICR software flags casualties and sends documents to the user for review.
When a user intends to upload additional documents, the APIs automatically generate new models to read and analyze the information. However, if the same types of documents are uploaded, the system employs previous techniques to scan and extract data.
Various Applications of Intelligent Character Recognition(ICR)
Intelligent Character Recognition (ICR) is particularly beneficial in converting information from physical documents into electronic formats. Several industries make use of ICR, including:
- NBDCs, banks, and financial institutions keep huge volumes of client data. ICR scans information from checks, forms, loan applications, and surveys to process them digitally.
- Educational institutions leverage ICR to interpret handwritten data and input it into systems for grading purposes.
- E-commerce stores and online businesses utilize ICR to gather electronic signatures and securely store them in databases for Know Your Customer (KYC) documentation purposes.
- In compliance with legal requirements, numerous insurance companies must digitize documents. By employing ICR for filing claims, transparency is significantly enhanced. The data remains accessible online and can be readily verified, simplifying policy renewals and facilitating the lodging of complaints for customers.
Intelligent Character Recognition Benefits
ICR is an adaptive technology that continuously learns and improves its ability to transcribe information into digital databases. With each exposure to new handwriting styles and fonts, it enhances its understanding and adaptability, resulting in more accurate transcriptions. It carried out the following benefits to companies:
- Organizations embrace ICR alongside transcription services, integrating it with diverse document types.
- With the implementation of ICR solutions, uploading data to cloud storage becomes more secure, as companies take diligent measures to protect information from unauthorized access.
- ICR services offer robust data verification and eradicate time spent on conventional data entry. It helps employees in becoming more productive.
- Furthermore, it excels at maintaining digital archives of records, finds extensive application in document management, and offers scalability as a solution.
Difference Between OCR and ICR
Organizations commonly receive a wide array of documents in diverse formats via email, including JPEGs, spreadsheets, and PDFs. Multiple formats present processing challenges, prompting the adoption of OCR technology for intelligent automation
Cognitive data capture solutions further enhance this process by adding human interpretation to the data and facilitating the organization of unstructured fields.
The debate surrounding OCR and cognitive data capture solutions is ongoing, and businesses have different reasons for choosing either ICR or OCR. However, the optimal approach often involves utilizing both technologies. Here are the key distinctions between them:
- OCR systems rely on templates for data extraction and do not utilize artificial intelligence neural networks, unlike ICR. Template-based optical character recognition (OCR) adheres to a predefined structure for inputting data, while cognitive data capture solutions acquire the ability to identify a wide range of formats through learning.
- OCR is well-suited for companies with standardized document structures, while ICR is adaptable and trained to handle frequent invoice changes.
- OCR software often entails project administration fees, whereas ICR technology is fully automated.
Intelligent Character Recognition (ICR) technology has emerged as a powerful tool for accurate data extraction, particularly in handwritten text. By surpassing the capabilities of Optical Character Recognition (OCR), ICR leverages Machine Learning algorithms and AI to intelligently analyze data from physical documents and forms, enabling the recognition of diverse handwriting styles and fonts. This advanced technology is crucial in scanning and extracting detailed information from paper-based documents, securely digitizing the data, and integrating it with business workflows.