With the introduction of AI techniques, OCR is looking like a legacy technology that plucks out content from images into readable text. Hence, OCR powered image to text converter can accurately recognize and transcribe text from various sources and change it into editable texts.
But here we have! How advanced OCR is processed and what is its role in data extraction techniques? Let us move a little bit more on a journey through why to how and hear from one of the AI experts about how AI impacted OCR technology.
What is OCR?
OCR is the short form of the optical character recognition.
With the help of this advanced technology, you can reuse extract text from images, scanned documents, printed forms, GIFs, or any other format that contains images. After that users can access and edit the original content of scanned documents by avoiding their manual data entry efforts.
There are some software combined with the OCR system to change the documents into readable texts. These involve optical scanners or specialized circuit boards. But here the question is still lightning in mind; what is the role of AI in OCR systems?
AI plays a crucial role in optical character recognition. It helps to identify various patterns of texts in images even in distorted or complex images. In this way, efficiency and accuracy sures from documents.
How Does OCR Work – Steps
- Step # 1 – Image Acquisition
- Step # 2 – Pre-Processing
- Step # 3 – Character Segmentation
- Step # 4 – Feature Extraction
- Step # 5 – Character Classification
- Step # 6 – Post-Processing
Image Acquisition –
Acquisition is the act of gaining possession. In the OCR work the first step is image acquiring. So take an image by using external sources like a camera, scanner, or any other software.
Pre-Processing –
The 2nd step of OCR working is the pre-processing. This is the method to improve the document quality. This factor uses the image to text converter that removes the thresholding, noise, blurring, and image baseline. This tool extracts the data even from low-quality images and saves you manual efforts.
Character Segmentation –
This third step (character segmentation) is the process that refers to distributing the text into individual characters. Commonly, it can be done in the system of OCR to recognize the characters from images or scanned documents.
Feature Extraction –
The third step is about feature extraction. It starts with the idea of extracting multiple features. The characters are also recognized on the basis of these features.
Character Classification –
The fifth and most important step is the character classification. This classification leads to the various categories and classes. As we have extracted features and characters by OCR service same as this transformed into the sentences.
Post-Processing –
The last step is to improve the accuracy of the final output. As we know the extracted text never be 100% efficient so the post-processing includes a dictionary to improve accuracy.
What Are Applications Of OCR & AI Data Extraction Technology?
There are some reasons why we take into account OCR and AI technology. So have a deep look at these!
- Finance: OCR helps to automate the data from financial documents. These include receipts, invoices, and analyst statements.
- Healthcare: In healthcare centers OCR picture to text converter are utilized. It enables to drawing of information from medical records, administrative tasks, patient care forms, and prescriptions.
- Legal: In the legal documentary including court records and contracts, the advanced AI OCR technique helps to digitize them.
- Retail: Purchase orders, invoices, and shipping documents are also optimized and supply chain operations with the help of advanced optical character recognition.
- Insurance: Insurance claims, expedited claim forms, risk processing, and policy documents, OCR automates the extraction of data
- Education: From the records of the students, assessments, presentations, reports, and transcripts OCR enables one to make an informed decision about data extraction.
Frequently Asked Questions (FAQs)
Who Invented OCR?
Emanuel Goldberg invented Optical character recognition. He traces its roots back to telegraphy.
How Artificial Intelligence Gives OCR a Boost?
Artificial intelligence has the potential to improve optical character recognition (OCR) by enabling more advanced ICR (intelligent character recognition) capabilities. It improves accuracy and efficiency and recognizes the diverse fonts, layouts, and styles.
Conclusion:
There are some steps that need to be taken with OCR technology. This process leads to the extraction of data from scanned or printed documents with AI capabilities. Image to text converters can recognize handwriting and different fonts into searchable text. This tool makes text extraction more efficient and easier.
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