Overview of Artificio

Key Benefits

Purpose of the Manual

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How to Use This Manual

Introduction

System Requirements

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Getting Started

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Understanding the Artificio Interface

Quick extraction model (QEM)

Introduction to model training

Introduction to data views

Google drive integration

Overview of design tile

Overview of Artificio's functionalities

Various Models – Document Understanding

Annotation Process for Artificio

Data Modelling details

Automation in Artificio

Automation

Artificio's Pricing

Pricing

Form Design Components

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Editable Forms

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<h1>Introduction to Layout NER Model for key pairs extraction</h1>Named Entity Recognition (NER) is a critical component of AI-driven text analysis. In Artificio, the NER model extends beyond traditional text-based entity recognition to include layout-based understanding, which considers the spatial organization of text in documents. This approach is especially useful in processing documents where the layout plays a crucial role in understanding the context, such as invoices, forms, and business letters.<h2>Understanding Layout NER and Its Significance</h2>Concept: Layout NER combines traditional NLP techniques with understanding the layout or format of a document. It recognizes entities not only based on textual content but also considering their position and relationship with other elements on the page.Use Cases: Key applications include extracting information from forms where data is presented in tables, or from documents like business cards where spatial arrangement is significant.<h2>Integrating Transformer Architecture</h2>Advancement with Transformers: Transformers revolutionize NER by using self-attention mechanisms, allowing the model to process each word in the context of the entire text. In layout NER, this includes considering the spatial relationships and layout.Benefits for NER: This results in a more nuanced understanding of text, enabling more accurate entity extraction from complex documents.<h2>Training Layout NER Model</h2><a target="_self" rel="" href="https://artificio.ai/help-support/data-modelling/Annotation-Process-for-Artificio/Annotation?menu=93">1. Data Annotation:</a>Select a diverse range of document types with varying layouts.Annotate entities considering both the text and their layout context. For instance, in an invoice, the ‘Total Amount’ might always be located near the bottom right.2. Model Training Steps:Upload the annotated documents to Artificio.Initiate the training process, where the model learns from the annotations, considering both text and layout.3. Evaluation and Fine-tuning:Test the model with a set of documents it hasn’t seen before.Analyze its performance and retrain with adjusted annotations if necessary.<h2>Practical Examples and Application</h2>Example 1: Extracting Information from Invoices.Exercise: Annotate key financial data like totals, dates, and vendor names on various invoice layouts. Train the model to recognize and extract these entities based on their position and text.Example 2: Processing Registration Forms.Exercise: Annotate personal information fields on different form layouts. Train the model to identify these fields by both their textual <a target="_self" rel="" href="https://artificio.ai/help-support/data-modelling/Annotation-Process-for-Artificio/Labels?menu=98">labels </a>and their positions on the page.<h2>Industry-Specific Use Cases</h2>Healthcare: Extracting patient details from medical forms where the layout is crucial for correct data interpretation.Legal: Identifying clause numbers and headings in legal documents where layout aids in understanding the document structure.Banking: Processing loan applications by extracting key financial and personal information from structured forms.<h2>Advanced Tips for Effective Training</h2>Consider Variations in Layouts: Train the model with as many different layouts as possible to enhance its adaptability.Focus on Contextual Relationships: Understand the relationship between entities and their position on the page for more accurate annotation.Iterative Training: Continuously refine the model by retraining it with new data sets and layouts to maintain high accuracy levels.Through this comprehensive training, users of Artificio will be equipped to effectively utilize the Layout NER model, harnessing both text and layout context for superior entity recognition. This skill is invaluable in processing a wide range of document types across various sectors, leading to more efficient and accurate data extraction and analysis.<h1>Extracting Table Line Items Using Layout NER Model</h1><h2>Introduction to Table Extraction with Layout NER</h2>In the realm of document processing, extracting data from tables is a common but challenging task, especially when dealing with varied layouts and formats. The Layout NER (Named Entity Recognition) model in Artificio addresses this challenge by utilizing AI to recognize and extract line items from tables, considering both text content and spatial layout.<h2>Understanding Layout NER in Table Extraction</h2>Concept: Layout NER for table extraction combines traditional NER methods with an understanding of the document's spatial layout, particularly focusing on the structure of tables.Importance: This approach is crucial for accurately extracting data from tables where the position of text plays a key role in interpretation, such as invoices, financial statements, or inventory lists to identify row (line-items) and columns (headers) from table structure.<h2>Steps to Extract Table Line Items Using Layout NER</h2><a target="_self" rel="" href="https://artificio.ai/help-support/data-modelling/Annotation-Process-for-Artificio/Introduction-to-Annotation-in-Artificio?menu=94">1. Data Preparation and Annotation:</a>Select documents containing the types of tables from which you need to extract data.Annotate these tables, highlighting the rows, columns, and individual cells, and <a target="_self" rel="" href="https://artificio.ai/help-support/data-modelling/Annotation-Process-for-Artificio/Labels?menu=98">label</a> them according to their content (e.g., item name, quantity, price).2. Training the Layout NER Model:Input the annotated documents into Artificio.Train the Layout NER model on this data, enabling it to learn the pattern of how information is laid out in tables.3. Model Testing and Validation:After training, test the model using different documents to evaluate its accuracy in extracting table line items.If necessary, adjust your annotations or add more samples to improve the model's performance.<h2>Practical Application and Examples</h2>Example: Extracting Product Details, Line Qty, Line Amount from Invoice Tables.Exercise: Users will annotate a set of invoices, marking product names, quantities, and prices in the table. The exercise involves training the model to identify and extract these line items accurately.Example: Processing Bank Statements.Exercise: Annotate bank statements, identifying transactions, dates, amounts, and descriptions in the tabular layout. Train the model to recognize and extract these details.<h2>Tips for Effective Table Line-Item Extraction</h2>Diverse Data Sets: Include a variety of table layouts in your training data to increase the model's adaptability.Attention to Detail: Ensure accurate <a target="_self" rel="" href="https://artificio.ai/help-support/data-modelling/Annotation-Process-for-Artificio/Introduction-to-Annotation-in-Artificio?menu=94">annotation </a>of table cells, rows, and columns for effective training.Iterative Improvement: Continuously refine the model by adding new examples and retraining, especially when dealing with complex or unusual table formats.<h3>Conclusion</h3>Using the Layout NER model for table line-item extraction in Artificio empowers users to tackle one of the more complex aspects of document processing. This tool opens possibilities for efficient data extraction from a variety of table formats, streamlining operations and enhancing data accuracy in multiple industries. By mastering this technique, users can significantly improve their document processing workflows, making the extraction of data from tables both reliable and efficient.

(NER) Model

Layout Named entity recognition Model (NER)

Headings