OCR in action – Loan Underwriting
OCR is the process of using technology to read characters from printed or handwritten text including from inside digital images of actual documents, such as scanned paper documents.
Its primary function is to read a document’s text and convert the characters into code that may be used for data processing.
Consequently, OCR has emerged as a critical component of modern business operations. By 2030’s end, the worldwide OCR market is expected to be worth $70 million.
Applied OCR is also commonly referred to as Intelligent Document Applications (IDA). Below, I list the most well-known applications of OCR across various use cases:
How does OCR work
Specifically, OCR systems utilize several key steps: Preprocessing, Character Identification & Feature Extraction, and Post Processing. A sample flow chart presents a 6-step OCR classification process.
- Image acquisition – This step involves scanning a physical document and uploading its digital copy into the OCR system.
- Preprocessing – This process involves the training data that the OCR model uses. Moreover, preprocessing includes thresholding (transforming a physical document into a binary image), normalization, and noise reduction.
- Segmentation – The segmentation technique aims to break a whole image into subparts, thereby enabling the character recognition apps to process the document easily.
- Feature Extraction – This step extracts the most relevant information from the text image, enabling the software to recognize the characters in the text.
- Classification – This process allows the identification of the character categories.
- Post-processing – The process aimed at the reduction of noise and errors in the converted document.
Applications of OCR
Banking
In addition, OCR facilitates the complete automation of underwriting, trade finance, risk management, NDTL management, and more.
Insurance
Furthermore, it enhances claim request processing and automation, resulting in higher claim settlements.
Healthcare
Additionally, NLP can be applied to OCR documents to automate medical transcription and reports.
Legal
Moreover, it enables the digitization of legal forms, business contracts, emails, and incorporation acts.
Logistics
Lastly, OCR automates the processing of packages, tracking, registration, and delivery.
Use Cases we help
At Macgence AI, we can proudly claim our exposure in delivering high-quality training datasets across all the above use cases. Whether it involves custom data sourcing or delivering OTS data for your plug-and-play needs, we can partner with you to become an end-to-end AI training data provider.
Here are some samples of use cases we solved for our client:
A Client Case
A global SIFI wanting to optimize their underwriting process.
Requirement
Sourcing 10,000+ bank statements across various languages for Doc OCR for its Loan Originating System.
Execution
Batch-wise sourcing of documents with constant client feedback on quality and PII redaction in line with the model’s guidelines.
Impact
Delivering 95%+ accuracy in PII redacted documents within 8 weeks, enabling the client to efficiently develop the model without fitting.
The Macgence Way
TAT
In conclusion, high-quality, compliant data is available at your disposal, providing the benefits of customization and quick delivery.
QUALITY
Our dataset goes through rigorous 2-level quality checks before delivery
COMPLIANCE
Moreover, we adhere to both the mandatory compliance requirements of HIPAA and GDPR.
ACCURACY
So Ultimately, we provide ~98% accuracy across different annotation types and model datasets.
NO. OF USE CASES SOLVED
Additionally, we have experience across a diverse range of use cases.
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