In business operations, extracting data is one of the important steps in document management. As businesses receive a large number of documents from various sources, such as clients, vendors, and internal divisions. With the volume of documents handled, businesses typically have standardized documents into digital formats such as text, JSON, or CSV, and the finance or accounting team has to convert image-based documents into that type of formats before processing or entering them into the system. The process may face some challenges, leading to a delay. This article will help businesses that are facing similar issues when extract data from image by providing several methods.
Why is it Necessary to Extract Data from an Image?
Image formats are not flexible enough; they are not searchable and often have large file sizes. As a result, image format cannot be the formats that businesses rely on for efficient data management. These types of documents are commonly found in image formats, such as:
– Identity cards
– Policy card
– Invoices
– Receipts
– Proof of payments
– Payslips
Businesses need to extract data from these documents like invoice numbers, dates, identity card details, transactions, and more. Most of these documents shared via email or business platform are in image formats. For example, when customers apply for a loan or submit an insurance claim, they typically upload their ID card and policy card. Then employees must manually input that data into the system. That’s why many businesses are now implementing advanced software to help extract data efficiently from these images.
Challenges of Extract Data from Image
1. Unstructured Data Formats
Images are unstructured data because they do not have a predefined layout or consistent format. Image formats frequently contain various formatting elements, including font size, style, tables, and complex structures. For example, invoices from two vendors may have different formats and layouts
2. Image Quality Issues
Image documents are commonly taken using smartphones or scanned documents. Some issues, like low resolutions, blurry images, skewed angles, and poor lighting, can hinder accurate data extraction. This can lead to misreading and misinterpretation, resulting in missing data and false extraction. Thus, when extracting data from an image, multiple checks are required to ensure accuracy.
3. Large File Sizes
Image formats have a large size, which can take more time to load, process, and download documents. It also consumes large storage space, especially when dealing with large amounts of paper or bulk documents. This obviously makes it difficult to extract, especially when done manually.
Methods for Extracting Data from Image Formats
1. Manual Method
Manual data entry involves inputting information one by one from image documents into systems such as internal databases or Excel. This method takes a lot of time and is error-prone, but if your business has a small amount of documents to handle, a manual method can be implemented.
Pro:
– No requirement for additional software that costs extra
Cons:
– Time-consuming
– Need extra effort and labor
– Human errors and prone to fraud
2. Image Converter
Image converters are software tools that convert images into formats like JSON, Excel, and CSV. These tools are widely available on the internet, making them easily accessible, and some even provide free conversion.
Pro:
– Easier data extraction
Cons:
– Low data accuracy
– High security risk, especially when documents contain sensitive information.
– Lack of fraud detection
– Limited free conversions; if you have a large number of documents, you need to pay, and it can be costly.
3. AI Solutions
Artificial intelligence (AI) is advanced software that can automate data extraction from all documents, including image formats. This method uses OCR (Optical Character Recognition) to read data contained in the documents quickly and accurately. For businesses that handle a large volume of many documents per day, implementing AI solutions for extracting data from images can be the best way to achieve efficiency.
Pro:
– Extract data quickly and accurately even with semi-structured or unstructured documents.
– Saves time and effort
– Minimize errors
– Avoiding Fraud
– Cost-effectiveness
– Multiple language compatibility
– High security level
Cons:
– Additional costs for software setup
– Required training for staff to adapt to advances in technology
AI software like Fintelite can be one of the best solutions to implement. It offers many benefits that can be a game changer for your business. You will get faster processing, high accuracy, fraud detection, and greater security.
Conclusion
That is the comprehensive overview of how to extract data from image. From the challenges to the methods that you can implement in your business. If you are dealing with big amounts of documents each day, you should consider moving to automation. This will improve all of your operational processes, especially data extraction.
