Overcoming Image Quality Issues in Data Extraction: Effective Strategies, Tools, & Technologies

Overcome image quality issues in data extraction by leveraging advanced preprocessing techniques and robust machine learning algorithms. Read the blog to learn about proven strategies, essential tools, and technologies to enhance accuracy.

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Overcoming Image Quality Issues in Data Extraction: Effective Strategies, Tools, & Technologies

Difficulties in extracting graphic data, such as low resolution, noise, blurriness, and distortion of figures, can lead to suboptimal decision-making, decreased productivity, and increased manual intervention, which, in turn, can affect your operations. 

These image quality issues can harm businesses striving to maintain high operational standards. 

We’ve compiled a list of practical strategies, tools, and techniques to help you overcome image quality issues in data extraction.

What is image quality?

Image quality encompasses pixel density, sharpness, color gamut, and freedom from geometric distortion and noise. In other words, it is based on how much an image represents visual information. 

In data extraction, an image’s quality directly affects the process’s efficiency and the reliability of the results. In high-quality images, the sharpness of the image allows the extraction algorithm to easily distinguish between texts, numbers, or any other required data. On the other hand, poor-quality images bring uncertainties and ambiguities, which contribute to errors and inaccuracies in the extracted data.

Docsumo ensures high accuracy rates in data extraction by reliably capturing and processing data from various document formats, including scanned images, PDFs, and digital documents. 

Read more: A comprehensive guide to image optimization for data extraction 

Common image quality issues impacting data extraction

Common image quality issues impacting data extraction

1. Low resolution

Resolution refers to the number of pixels per unit area in an image. Higher-resolution images contain more pixels, resulting in greater detail and clarity. When an image is low quality, the details cannot be well-rendered, which poses problems when performing tasks such as extracting small texts or patterns. 

In healthcare, using low-resolution scans of handwritten prescriptions and medical forms to extract patient data can lead to mistakes and inaccuracies in patients’ diagnoses and treatment plans.

2. Poor lighting and contrast

Poor lighting and contrast, especially in images, may make reading written text or details on the extracted data difficult. 

For instance, while extracting data in the financial sector, receipts taken in low light have low contrast, which may be inaccurate when capturing transaction details required in reporting expenses.

3. Physical damage to documents

Any physical damage, such as a tear, crease, or spot, affects the image’s readability and data extraction from graphic images and documents.

Physical damage to documents affects data accuracy when extracting data from logistics, as delivery information described in shipping documents can lead to a delay in the shipment or incorrect stock checking.

4. Color distortion

Color distortion in an image impacts the shape, clarity, and look of the text and any visual object, which challenges database extraction algorithms. 

For instance, in data extracting in healthcare, errors in color during medical images or chart scanning could distort diagnostic information and complicate patients’ treatment.

5. Noise and artifacts

Noise and image artifacts, such as speckles or scratches, will likely obscure text and details, impacting data extraction accuracy. Furthermore, if an image of a machined part contains noise or artifacts, it becomes challenging for quality control systems to accurately detect surface imperfections or dimensional inaccuracies.

Strategies for overcoming image quality issues

Strategies for overcoming image quality issues

1. Manual adjustments

  • Scanner settings calibration: To improve the quality of scanned documents, calibrating scanner settings such as resolution, color depth, and contrast can enhance the quality of scanned documents. Adjusting these settings confirms that snaps are taken in the best quality to avoid possible errors that may be misleading in extraction
  • Document preparation: Straightening pages, removing staples or paper clips, and ensuring even lighting facilitates smoother data extraction processes by making images clearer and more legible

2. Preprocessing techniques

  • Good quality authentic source: Ensure the source image is clean, undamaged, and printed with high-contrast ink. A clear, original image lays the foundation for accurate OCR conversions
  • Deskewing: Correct skewed images using image editing software or scanner settings. Maintain horizontal alignment between text and graphics for easier interpretation
  • Noise reduction: Apply noise reduction filters or settings to remove unwanted image artifacts. Try experimenting with different reduction levels to find the optimal balance between noise reduction and image clarity

3. Optimal scanning settings

  • Scale images to the right size: Choose a suitable resolution, preferably the 300 DPI one, to be specific, which will be clear enough while being relatively small
  • Increase contrast: Enhance contrast and density to improve text visibility against the background. Boosting contrast brings more clarity to the output, facilitating accurate OCR results
  • Binarize image: Binarizing pictures helps reduce the complexity of the subsequent stages of image processing. The conversion can be done using various software tools, or when scanning the document, make sure that the text is easily distinguishable from the background

4. Regular maintenance of scanning equipment

  • Keep equipment clean: Regularly clean scanner glass and document feeders to remove dust, dirt, and debris. Clean equipment produces clearer images and prevents artifacts that can affect OCR accuracy
  • Calibrate regularly: Calibration ensures accurate and consistent scan results. You should run calibration checks on your scanner to ensure the images have the right outlook, orientation, and angle
  • Update firmware and software: Regularly update the scanner’s firmware and software to obtain the latest updates, enhancements, and relevant features. These updates can also improve reliability and the ability to work with the OCR software

5. Training and calibration

The first step to maintaining consistent image quality and extraction accuracy is to train your full-time employees (FTE) in scanning and data extraction methods

Educate them on scanning methods, image management, calibration, and combining manual adjustments (correcting scanner settings) with software (image enhancement tools) for accurate image capture and processing.

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Learn how data extraction can 10X your business process efficiency.

Tools and technologies for enhancing image quality

Manual efforts to enhance image quality are time-consuming and error-prone. Advanced image enhancement tools use image processing features and AI and deep learning improvements that allow them to correct common errors in the quality of received images and to adjust them for further data recognition.

Tools and technologies for enhancing image quality

1. Adobe Photoshop

Adobe Photoshop is widely used for image editing and manipulation. It has numerous features and options that help boost the quality of the image, such as sharpness, noise elimination, and hue adjustment. 

Operations, IT, and underwriting teams use Photoshop to rectify image quality and legibility to enhance images before data extraction.

2. ABBYY FineReader

In addition to its robust text recognition capabilities, ABBYY FineReader offers features such as image preprocessing and enhancement filters. 

You can automatically correct common image quality issues, such as blurriness and noise, before extracting text, ensuring more precise and accurate OCR results.

For a more efficient solution for OCR tasks, consider Docsumo, an efficient ABBYY FlexiCapture alternative. While both platforms offer OCR capabilities, Docsumo stands out with pre-trained APIs, easy processing, numerous integrations, and affordable and transparent pricing. 

Here’s how Docsumo stands apart: 

  • Users can perform OCR using pre-trained APIs that come with the service, reducing the configuration level needed for the process
  • Docsumo offers powerful preprocessing features to improve image quality before data extraction, making the process more accurate 
  • Its compatibility with different business applications also makes it even more helpful as it easily enables the integration of business processes, making work much easier and faster
  • Transparent pricing and pay-as-you-go options 

3. OpenCV

OpenCV (Open Source Computer Vision Library) is an open-source computer software library for real-time computer vision and machine learning. 

It offers features designed to improve areas, such as image noise removal, contrast-boosting features, and edge detection. The machine learning software library can be incorporated into different use cases to enhance image resolution and data extraction techniques.

4. Tesseract OCR

Tesseract OCR primarily focuses on text recognition and offers preprocessing features to enhance image quality before OCR. Tesseract’s built-in image binarization and deskewing functionalities improve the clarity of scanned documents and optimize OCR accuracy.

5. Neural networks and machine learning models

Neural networks and machine learning models are high-level, complex mathematical models used for image data analysis and apply corrective mechanisms to image issues like blur, noise, and color shift. 

By training on large datasets, neural networks can intelligently adjust images, optimizing them for accurate OCR and improving the reliability of data extraction processes.

How to ensure consistent image quality in data extraction

Maintaining high image quality is important for successful data extraction workflows. This involves regular equipment maintenance, like cleaning, calibration checks, firmware updates, and staff training. 

1. Standardized scanning protocols

Adopting standard scanning practices helps maintain uniformity and consistency in the images acquired. Lay down protocols for scanning documents, such as the appropriate resolution, colors used, and the format in which documents are scanned. 

Regulate and control the variability scanning protocols to prevent distortion of image quality and inconsistency in data extraction methods.

2. Quality checks and controls

Establish inspection points throughout the data extraction workflow to inspect scanned images for clarity, readability, and accuracy. 

3. Employee training

Training is essential to help the employees provide the best setting and use of scanning and imaging equipment. Establish staff-training programs to explain the procedures and guidelines for handling documents, the proper methods of scanning the documents, and the quality-control measures. The staff should know how to use the scanning equipment properly, fine-tune them if necessary, and resolve potential problems.

4. Technological upgrades

Technological upgrades help improve image quality, enhance data extraction activities, and remain relevant. This includes ensuring that the scanning and imaging equipment is up to date in terms of technology due to the regular technological enhancements. The target is to remain up-to-date on new advances in the hardware and the software used to perform scans, as well as the methods involved in the actual processing of the images. 

5. Feedback loop

Seek employee feedback about how scanner protocols, quality assurance procedures, and staff education are effective. When embracing feedback and improvement are constant processes, it becomes easier to improve the existing processes and provide consistently high image quality in extracting data.

Enhancing data accuracy through improved image quality

Poor image quality can impact productivity and operational efficiency. Strategies like manual adjustments, software solutions, optimal scanning settings, and specialized OCR settings overcome these challenges and help address image quality issues in data extraction.

Advanced image processing software enhances image quality to retrieve data from specific fields and present it in a structured format for further analysis and processing.

Docsumo is one of the best data extraction software with over 99% accuracy rate, robust OCR technology, and pre-trained API. It captures and extracts data from various document formats, including scanned images, PDFs, and digital documents, and offers advanced solutions to automate document processing and ensure high-quality data extraction. Customizable workflows allow users to tailor extraction processes to their needs.

Get in touch with Docsumo today to take the next step in upgrading your data extraction processes.
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Written by
Karishma Bhatnagar

Karishma is a passionate blogger who comes with a deep understanding of Content Marketing & SEO tactics. When she isn’t working, you’ll find her in the mountains, experiencing the fresh breeze & chirping sounds of birds.

How can businesses improve image quality for better data extraction?

Businesses can improve image quality for better data extraction by implementing best practices such as using high-resolution images, ensuring proper lighting conditions, and minimizing noise and artifacts. Purchasing better scanning equipment and improving image processing would improve the overall image and increase the degrees of accuracy of the information extracted.

What are common image quality issues that affect data extraction?

Some of the biggest challenges of image processing in data extraction are low-resolution images, bad image brightness/contrast, document physical damages, and image noises/artifacts, which lead to inaccuracy and errors in the extracted data and affect the reliability of data extraction.

Which tools are recommended for improving image quality in data extraction?

Tools like Adobe Photoshop, ABBYY FineReader, OpenCV, Tesseract OCR, neural networks, and machine learning models offer features for enhancing image quality, such as noise reduction, contrast enhancement, and image binarization.

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