Benefits of Automated

Data Extraction

eMender’s cross-functional nature ensures data security and isolation, making it a reliable choice for businesses
looking to enhance their document management processes.

How it Functions

Benefits of Automated Data Extraction

eMender SAAS based cloud-native platform created to automate and streamline document processing and data extraction. It leverages advanced OCR, machine learning, and GenAI technologies to provide accurate and efficient data handling.

  • intelligent

    Improved Accuracy & Efficiency

    Advanced algorithms identifies inconsistencies, minimizes errors, ensuring accurate data and increased efficiency.

  • Data

    Cost Savings

    By reducing the need for manual data entry, businesses can lower operational costs. Automation also decreases the likelihood of costly errors.

  • cloud

    Scalability

    Automated systems can handle large volumes of data, making it easier to scale operations as the business grows.

  • Consulting

    Enhanced Decision-Making

    Automated data extraction provides the insights needed to identify trends and opportunities and make informed decisions.

Be The Pioneer With AI Automation

Data is a vital to businesses, but managing the constant flow of information can be daunting. So, we use automated data extraction uses technologies like OCR, ML, and NLP to streamline this process, turning raw data from various sources into sources into actionable insights without manual effort. This approach helps businesses efficiency handle large volumes of data and focus on deriving value from it.

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IDP Preprocessing Steps Track your Business with Market Insights

Our automated platform exclusively streamlines the data processing with high-quality data extraction through customised Intelligent Document Processing for effective business with OCR and MLA.

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STEP 1

Document Quality Evaluation

  • Initial Assessment: The quality of the document is assessed using machine learning algorithms. This involves checking for factors such as resolution, clarity, and presence of noise or distortions.
  • Resolution Adjustment: If the document’s resolution is below the required threshold, algorithms upscale the resolution to improve clarity.

STEP 2

Noise Reduction and Cleaning

  • Noise Removal: Techniques such as Gaussian blur or median filtering are applied to remove noise from scanned documents.
  • Binarization: Converts images to binary format (black and white) to enhance text readability, making it easier for OCR to process.

STEP 3

Skew Correction and Alignment

  • Skew Detection: Algorithms detect any tilting or skewing in the document.
  • Rotation and Alignment: The document is rotated and aligned properly to ensure text is horizontally aligned, improving OCR accuracy.

STEP 4

Segmentation and Layout Analysis

  • Text Segmentation: Identifies and isolates different sections of the document, such as headers, footers, paragraphs, and tables.
  • Layout Analysis: Analyzes the document structure to understand the spatial relationships between different elements.

STEP 5

Text Enhancement

  • Contrast Adjustment: Enhances the contrast between text and background to make characters more distinguishable.
  • Sharpening: Sharpens the edges of characters to improve OCR recognition.

STEP 6

Optical Character Recognition (OCR)

  • OCR Application: Converts the pre-processed image into machine-readable text.
  • Character Recognition: Identifies individual characters and words, ensuring high accuracy in text extraction.

STEP 7

Error Detection and Correction

  • Spell Check: Automated spell-check mechanisms identify and correct common OCR errors.
  • Contextual Analysis: Uses natural language processing (NLP) to ensure the extracted text makes contextual sense, correcting any misinterpretations.

eMender Automates Document-centric Processes using AI

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