Extracting data from images has become one of the more critical tasks for any business whose core is processing scanned documents or ID cards or invoices, etc. This process has to be fast, reliable, and scalable in the data-driven world.
However, if the methods and tools are incorrect, this may pose great challenges to businesses. What are the best approaches to extract data from images, whether traditional or advanced? Which are some recent powerful solutions to help streamline this process?
This article will highlight the challenges included in manual data extraction methods and explore some of the best ways of data extraction.
Challenges of Using Manual Data Extraction Techniques
Manual extraction has been the case in many organizations. Many organization are still extracting data manually leading to huge financial loss due to high human errors. This is affecting the brand image of the companies in the market. There are so many other issues with Manual Data Extraction Techniques some of them are as follows.
Time-Consuming: Manually reviewing and extracting information from each image becomes time-consuming, especially with businesses dealing with large volumes of data. The increasing number of documents exponentially adds to the effort, making it inefficient.
And also Receipt data extraction involves entering all relevant information from the receipts into the system for additional processing. This can be accomplished manually or through the use of receipt data extraction software or tools.
Susceptible to Human Errors: Manual data extraction is extremely error prone which leads to contrastive data. It consequently impacts on decisions and operational efficiency of the business due to human errors such as miskeying or misinterpretation of data.
Very High Manpower & Labor Costs: The more the documents to be processed manually, the more manpower it consumes. Labor costs can surge rapidly, making manual data extraction pricey and utterly unsustainable in the long-term end.
Lack of Consistency: Different people working on extracting different data means usually the consistency in the extraction and recording will often be missing. This creates inconsistent data, which sometimes brings additional problems related to the accuracy of the data.
Slow Turnaround: Data extraction being manual prevents organizations from accessing crucial information as promptly as possible. Such a delay is very detrimental when rapid access to information is needed in compliance or operational decision-making.
Here are 4 Best Ways to Extract Data from Image
1. Using Basic Image Editing Tools
Most businesses use simple image editing tools, such as Photoshop or GIMP, to try to get data from images. These can clean up an image, make it a little easier to read, but take huge time and effort.
Small-Scale Use Only: Basic image editing is the best for small-scale activity where only a few images need to be processed. The cropping, highlighting, or adjustment of the image enables the user to read it easily. However, these tools are manual and thus take a long time in the processing process.
No automation: the tools are not developed for mass extraction of data. Businesses utilizing image editors for the process of extracting data from hundreds or thousands of images will soon start noticing how unbecoming the process is, almost unworkable in actual practice. There is no automation; the manual selection of text slows down productivity.
2. Data Extraction via Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is one of the many most common methods for extracting data from images. OCR enables using OCR technology to automatically convert printed or handwritten text from images into machine-readable text, which will speed up the extraction process.
OCR works; it scans through images, reads out the characters, and converts this into editable text. It recognizes letters, numerals, and symbols in shapes and patterns, so retrieving information from standardized documents like invoices, forms, and ID cards becomes very easy.
Advantages of OCR: OCR is a fast and automated technology, making it an ideal choice for businesses that need to manage large volumes of paper documents. It can achieve an impressive accuracy rate of 99% in data extraction.
For bank statements OCR offers more than just basic data extraction. It enables financial institutions, accounting firms, and businesses to automate their workflows, make well-informed decisions, and reduce risks.
Moreover, OCR saves time by eliminating the tedious process of manual data extraction. Most importantly, it allows companies to work with scanned documents, transforming them into searchable and editable files.
Limitations of OCR: Although it is powerful, OCR is not without its limitations. Poor-quality images, highly complex layouts in the documents, and written text all place a strain on OCR systems. OCR can also fail to reach high accuracy when texts include a number of languages or different content formats. To overcome all these challenges you will need to have advanced and powerful OCR software such as KlearStack.
3. AI-Powered Data Extraction Tools
While OCR is indeed very effective in most applications, AI-based data extraction tools take that a bit further. This tool includes ML that has the capability to learn about a wide variety of document types, structures, and layouts so that accuracy increases with time.
How does AI-Powered Extraction Really Work? AI-powered data extraction tools work based on the input from the machine learning model, which reads data in a form that is not just pattern recognition.
Training the system from a large range of documents helps in the capabilities of its recognition to all that is very complex, even handwritten notes, and adaptation to new formats of data.
Accuracy Over Time: AI-based extraction learns from mistakes. The more data the system is processing, the more efficient it will be in dealing with complex documents, therefore it will yield better results than traditional OCR solutions. It is well-fit for finance, health care, or the legal service business because the accuracy of the data is highly crucial.
Use cases for AI-powered tools are the extraction of unstructured data. For example, it can be used to extract content from handwritten medical forms, invoice formats with multiple columns, or legal contracts which contain various formatting elements. Being very adaptive, they improve as more data is processed.
4. Data Extraction through APIs and Custom-Built Solutions
There are other companies that have resorted to data extraction through APIs or custom-built solutions. Such solutions come with flexibility and customization, allowing the company to fine-tune its data extraction process according to their needs.
API Integration: Through API-based tools, businesses have the opportunity of automating this extraction process by linking up directly with their current software. This link encourages real-time extraction and may smooth work processes between departments.
Custom Solutions: If the off-the-shelf software can't fulfill certain needs, then companies have to custom build data extraction solutions. As they provide complete control as well as maximum flexibility, it is suitable for large enterprises with specific needs. However, the investment in the development and maintenance of such solutions goes extensively high, and it's more feasible for large organizations.
5. KlearStack: The Best Way to Extract Data from Images
KlearStack is an ideal as a powerful tool for businesses to optimize their data extraction processes. It not only possesses extensive OCR capabilities but also in-built AI features, machine learning abilities. The strength of this technology makes it possible for the continuous improvement and learning abilities that KlearStack has and enables highly accurate data extraction throughout various documents.
KlearStack has an ML capability that automatically improves its performance with time. It learns from past data to increase the speed along with the accuracy of extraction work.
KlearStack performs well in complex image formats such as PDFs, scanned documents, and invoices. The handwritten forms, multi-language document, and irregular layouts also do not affect its performance.
Businesses can automate extracting data with minimal interference using KlearStack. KlearStack seamlessly integrates into your workflow and offers real-time analytics, while the software can also be customized for whatever uniqueness exists in a particular business. With a 99% level of accuracy, KlearStack is the go-to for organizations dealing with large volumes of image-based data.
Conclusion
Image extraction sometimes appears to be a challenge, especially in the case of business considering large data and complex document formats. Needless to say, manual extraction, image editing tools, and classical OCR do find use but very clearly are less effective as compared to such powerful AI-based solutions like KlearStack.
With the presence of AI and ML capability, KlearStack can be used by businesses for extracting data quickly, accurately, and efficiently. Whether you are processing invoices, ID cards, or complex forms, KlearStack simplifies your task, saving valuable time and resources.
