Handl is revolutionizing the way businesses extract data from documents by leveraging the strengths of artificial intelligence (AI) and machine learning (ML) technologies, complemented by human expertise through a Human-in-the-Loop (HITL) validation service. This unique combination of cutting-edge technology and human input ensures the highest level of accuracy and efficiency in data extraction and document processing. In this article, we will delve into the stages of Handl’s HITL workflow and discuss the depersonalization methods used to maintain client data security.

HITL Workflow: Fusing AI and Human Expertise for Precise Data Extraction

The HITL approach employed by Handl addresses the limitations of AI and ML algorithms by incorporating human expertise into the process. While AI-driven solutions have made significant strides in data extraction, they may still struggle with certain scenarios, such as handling poor-quality images, complex layouts, or varying document formats. By integrating human input, Handl’s HITL workflow ensures that these challenges are effectively addressed, leading to more accurate and reliable data extraction results.

Handl’s HITL workflow consists of two main stages to guarantee accurate data extraction results:

Stage 1: Assessing OCR Results

In this stage, human operators receive a pair of “cut-out field + digitized text” and evaluate the correctness of the Optical Character Recognition (OCR) results using “Yes” or “No” buttons. Each field is assessed by multiple operators, and the digitized text is considered correct only if all responses converge.

Stage 2: Manual Data Input

If at least one operator selects “No,” the cut-out field is sent for manual data input. Operators input the text using widgets and dictionaries. For example, they must choose a date from a calendar, and the car model must strictly correspond to the brand selected in the previous field. The algorithm requests new responses for the field from different operators until a consensus is reached.

Depersonalization: Safeguarding Client Data Security in Handl’s Workflow

Handl takes data security seriously and employs depersonalization techniques to ensure the protection of client personal information:

Step 1: Decomposition and Shuffling

To safeguard client personal data, Handl applies decomposition and shuffling methods, breaking down the data into smaller components and rearranging them to prevent unauthorized access.

Step 2: Manual Recognition and Anonymization

Handl’s module functionality includes manual recognition of document parts, data anonymization, and customizable confidence threshold settings for the ML model, ensuring that sensitive information is protected during the data extraction process.

Step 3: Automatic Field Separation

Before being processed by human verifiers, documents are automatically divided into separate fields. This step further enhances data security by ensuring that no single operator has access to the complete set of personal information from a document.

Adopting Handl’s Human-in-the-Loop Validation Service for a Future-Proof Data Extraction Solution

Handl’s innovative use of HITL in data extraction harnesses the power of AI and ML while leveraging human expertise to achieve unparalleled accuracy and efficiency in document processing. The combination of these cutting-edge technologies and the rigorous depersonalization methods employed by Handl ensures that businesses can trust their document processing solutions to maintain the highest standards of data security.

By integrating Handl’s HITL approach into your data extraction workflows, you can revolutionize the way you handle document processing, improve overall efficiency, and safeguard sensitive information. Stay ahead of the curve by adopting Handl’s cutting-edge data extraction techniques and experience the transformative impact of their AI-driven, human-enhanced solutions.