Our Projects

Project Background

Introduction:

In the modern financial landscape, organizations are constantly seeking innovative ways to automate their document processing workflows. Manual extraction and processing of data from invoices, receipts, reports, and other financial documents is not only time-consuming but prone to human error. Leveraging Optical Character Recognition (OCR) combined with deep learning models can significantly streamline this process, making it more accurate and efficient. In this project, we utilized deep learning-based OCR technology to automate the processing of financial documents for a financial company, improving both speed and accuracy.

Problem Faced by the Client:

The client struggled with manual data extraction from financial documents like invoices and receipts, which was time-consuming and prone to human error. They needed to process a large volume of documents daily, making the manual approach inefficient and unsustainable. Additionally, they required a solution that could deliver accurate results quickly to keep up with their operational demands and ensure seamless workflow integration.

Proposed Solution Using OCR and Fine-tuning on Custom Dataset:

Our solution involved deploying an OCR model that has been fine-tuned on a custom dataset of financial documents, including invoices, receipts, and financial reports. Standard OCR models typically struggle with specialized terminology, formatting, and complex layouts found in financial documents. Therefore, we trained the model on a carefully curated dataset tailored to these unique characteristics.

1. Data Collection and Preprocessing: The first step involved gathering a diverse set of financial documents from the company's archive. These documents were scanned and labeled with the relevant text annotations, such as invoice numbers, total amounts, dates, and line-item details.

2. Model Training: We employed a deep learning-based OCR model for the recognition purpose. By fine-tuning the model on our dataset, it learned to recognize financial jargon and deal with complicated layouts that often include tables, multi-column formats, and logos.

3. Accuracy and Optimization: Through iterative training and hyperparameter tuning, we ensured that the model could recognize key information accurately, even in low-quality scans. Post-processing steps like text extraction and alignment ensured the final output was clean and usable.

Deployment on AWS:

To ensure the solution was scalable, we deployed the OCR model on AWS, leveraging several services for seamless integration and performance:

1. Amazon EC2 Instances: We used powerful EC2 instances to host the OCR model, ensuring high-speed processing even for large batches of documents.

2. Amazon S3: Scanned financial documents were stored in Amazon S3, making it easy to access and manage large amounts of data.

3. AWS Lambda: To automate document processing, AWS Lambda was used to trigger OCR processing as soon as new files were uploaded to the S3 bucket, creating an efficient, serverless workflow.

4. Amazon API Gateway: We exposed the OCR processing logic as a REST API through API Gateway, allowing the financial company to integrate the solution with their existing systems for real-time data extraction and analysis.

Benefit to Users in Saving Time:

One of the major benefits of this solution was the substantial time saved in document processing. In a typical manual workflow, financial professionals spent hours reviewing, extracting, and inputting data from scanned invoices and receipts. With OCR automation, this process was reduced to just a few seconds for each document, dramatically increasing operational efficiency.

• Increased Speed: By automating data extraction, financial professionals no longer needed to manually transcribe text or extract critical data fields.

• Error Reduction: The deep learning-based OCR model consistently provided accurate results, minimizing human error that could result from misreading handwritten or poorly scanned documents.

• Better Resource Utilization: Employees could now focus on higher-value tasks like analyzing financial trends or building client relationships, rather than getting bogged down in repetitive document processing.

Conclusion

If you’re looking for a similar OCR-based solution tailored to your financial document processing needs, contact us today to discover how we can help you automate workflows, reduce errors, and save valuable time. Let's make your document processing smarter and faster with deep learning and OCR technology.