Data in today’s digital age comes in various formats. It could be in an Excel sheet, scanned PDFs, Word documents, scanned images, or more complex formats such as JSON or XML. But extracting data from image feels more challenging than others. Why? Because the unstructured content, skewed letters, and blurry images are difficult to process.
If we talk about data extraction from images, then these images are obtained from various sources. These might be a scanned copy of a report from your office scanner, a mobile screenshot of a passport or driving license, or photos taken from your mobile of your certificates. It can be handwritten notes, invoices, bills, etc. As we can see, data in image format can be found in many life scenarios.
But data extraction from images remains a challenge for us due to inconsistent document layouts, poorly scanned documents, and skewed letters, which make data extraction difficult. The manual method of data extraction is slow and full of human errors, which reduces work efficiency. But with the rise of advanced technologies such as Artificial Intelligence, Machine Learning, Intelligent Document Processing, and OCR, extracting data from images has become easy.
In this blog, we will discuss the challenges associated with image data extraction, tools and technologies for extracting data from images, how manual image data extraction is not reliable, the best tools you can consider for image data extraction, and how AlgoDocs is the best tool for extracting data from images. Let’s explore.
Image data extraction means to capture and extract data from an image document using technologies such as AI, IDP, and OCR. These image documents can be in the form of JPG, PNG, or other image formats. The extracted data is later stored in a structured format and utilized for various business activities such as analytics, record keeping, decision-making, etc. One of the crucial aspects of image data extraction is that modern businesses thrive on data. The more accurate data extraction from images is, the better the business results.
An image can carry a wide variety of data. Here are some major types:
Handwritten Notes (Scanned) –
Handwritten notes written on paper and scanned with a scanner or mobile device are very common types of image data. Personal notes, bills, invoices, memos, patient forms, college admission forms, etc., are good examples of images containing valuable data.
Scanned Documents –
Documents are scanned in various scenarios. This could be in the office, where you need to scan a sales report or an ID card such as passports, driving licenses, etc., for verification purposes or any other documents for business or personal needs.
Mobile Screenshot Images –
A mobile device has become the backbone of our digital life. Except for any personal images, we also tend to carry lots of documents and screenshots of documents on our mobile devices. In many scenarios, we tend to take lots of screenshots of documents or web pages for business and personal use.
Computer Screenshot Images –
A screenshot taken from a sales report, dashboard, or document is often in image format. These images contain valuable business information, and processing and extracting data from these images is essential for business activities.
Image files containing valuable data often come in the form of scanned documents, photographed documents, screenshots, etc. These documents can be of various types such as:
Invoices –
Talking about image data, what can be a more useful example than an invoice? An invoice contains data such as item, price, address, invoice number, etc. Generally, invoices are generated as docs or PDF files. But sometimes the same invoice documents are scanned or their pictures taken for sharing and record-keeping purposes. Extracting data from these types of images becomes important for many reasons.
Bills and Receipts –
A bill is used in many B2B and B2C settings. It’s a proof of sale from the seller to the customer. This contains details such as vendor name, item description, billing date, payment info, etc. A general bill or receipt document can be a physical copy of the actual bill or receipt generated in PDF or document formats. But sometimes this can be in the form of JPG images or screenshots as well.
Certificates –
Educational certificates often provided by educational institutions, colleges, or schools are generally available in physical formats. But sometimes we carry them in the form of scanned images or photographs as well.
ID Cards –
ID cards are crucial documents for various types of KYC verification and other business activities. While a physical identity card such as employee ID, passports, driving licenses, etc., can be found in physical formats, sometimes we need to carry these documents in the form of scanned images, pictures, and other digital formats for ease.
Bank Statements, Bills, and Others –
We can also see the example of bank statements or various types of bills which are often available in physical paper form and are often carried in image, screenshot, or scanned document formats.
While image data extraction is important, there are many challenges that persist in extracting data from images. The challenges include:
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Poor Image Quality –
One of the major problems with image data extraction is poor image quality. Poor-quality image data is difficult to capture by human eyes as well as by automated data extraction software such as OCR and Intelligent Document Processing. This can lead to data errors during extraction.
Handwritten Texts –
Handwritten notes or text that have been converted into an image file are difficult to extract. Since every individual has unique handwriting patterns—some very clear, some complex—this leads to inaccuracy in data extraction.
Complex Layouts –
Complex layouts of documents are another challenge where image data extraction becomes more complicated. Factors such as tables, columns, fields with different styles and formats can create issues with data extraction.
Language Variability –
Image data can be multilingual, and technologies such as OCR can sometimes find it difficult to capture and extract data from multilingual documents. Even in manual data extraction, extracting data from multilingual documents isn’t always possible, as not every individual understands more than one language. This creates a hurdle in image data extraction.
Manually extracting data from images can lead to many problems which can hinder the growth of a business. Here are a few reasons why businesses should not consider extracting data from images using manual methods:
Time-Consuming –
Manual data extraction from images is a time-consuming process. But tools like OCR and IDP make it fast and accurate.
Error-Prone –
One of the biggest reasons why manual data extraction doesn’t work is because it is full of human errors.
Cost –
Manual data extraction is costly because you need to deploy more human power to extract data from images. This increases operational and training costs for the company.
Non-Scalable –
The manual data extraction approach lacks flexible scalability. As document volume increases, you can’t instantly hire, train, and deploy manpower for data extraction tasks.
Modern businesses need a growth-oriented approach where data extraction work is done with accuracy and speed. But manual methods lack these qualities. That’s why customized OCR tools and advanced AI-based intelligent document processing platforms make image data extraction more efficient.
As we know, image data extraction with a manual approach is very slow and inaccurate. But recent advancements in data processing technologies such as Zonal OCR and Intelligent Document Processing with AI and ML have made data extraction more efficient.
- Optical Character Recognition (OCR) – Recognizes text from scanned or photographed images.
- Artificial Intelligence (AI) – Helps detect patterns, classify data, and improve accuracy.
- Machine Learning (ML) – Learns from past errors to improve future performance.
- Natural Language Processing (NLP) – Helps interpret the meaning of extracted text.
- Intelligent Document Processing (IDP) – Combines OCR, AI, and NLP to automate entire workflows.
These technologies can handle complex layouts, handwritten text, and even noisy backgrounds, making them far superior to traditional methods.
There are several tools available for image data extraction, each with its strengths. Here are the top 5:
- AlgoDocs
- AI-based intelligent document processing engine.
- Highly accurate when it comes to image data extraction.
- Best for all-sized businesses.
- Great for business workflows and large-scale operations.
- Supports structured data output like JSON, Excel, and CSV.
- Third-party integration with many business apps, CRMs, and ERP tools.
- Tesseract OCR
- Open-source OCR engine developed by Google.
- Best for developers and tech-savvy users.
- Works well with high-quality scanned text.
- Nanonets
- Can extract data from images.
- Recognizes text in scanned PDFs.
- User-friendly interface.
- Best suited for large enterprise business settings and personal use.
- ABBYY FineReader
- Offers OCR and PDF conversion.
- Highly accurate with complex documents.
- Good for enterprises.
- Docsumo
- Good for various data processing activities such as PDF extraction, image extraction, etc.
- Can be integrated with third-party apps.
- Useful for note-taking and quick tasks.
- Recommended for large-scale businesses.
AlgoDocs can be considered the best tool for image data extraction for a number of reasons. But one of the most distinguishing features that makes AlgoDocs unique from others is its GenAI capability, which makes data extraction very easy. Here are a few noticeable features of AlgoDocs for image data extraction:
- Automation – Automates complex data extraction tasks from image formats.
- Accuracy – Uses AI and ML models to enhance precision, even with handwritten or poor-quality images.
- Scalability – Handles thousands of image files in bulk without compromising speed.
- Ease of Use – Simple setup with a user-friendly UI.
- Integration – Easily connects with other business tools and systems.
Unlike generic OCR tools, AlgoDocs is designed with business needs in mind—especially when dealing with financial, legal, logistics, or government documents that exist primarily as image files.
Data extraction from images is a critical process, as it can contain lots of valuable business and personal data. The manual data extraction method fails to deliver the speed and accuracy that is paramount for a business. But AI-based data extraction tools such as AlgoDocs come in very handy when you want to improve business document workflow efficiency and achieve 100% data accuracy and speed.
The rise of AI and ML has greatly improved data extraction capabilities, and the rise of technologies such as Intelligent Document Processing and Zonal OCR has made automating data extraction even more productive.