Note:

Reference values (prefixed by @ or @@) as well as static values (like 34, 56.90, etc.) can be passed as values to the Value parameter.

Note:

• This rule is commonly used to provide an input for subsequent rules within the ontology.

• The "Convert Amount in Words to Number" rule serves as a bridge between textual and numerical data representations, facilitating seamless data analysis and validation.

Note:

• The "Evaluate Expression" rule offers a powerful way to perform dynamic calculations and logic within the ontology.

• Expressions can be as simple or complex as needed, allowing for various mathematical and logical operations.

• Referenced fields within the expression must have been previously extracted and are available for use in the calculation.

Note:

• The "Extract Table Column Values Based on Line Number" rule streamlines the extraction of specific data from structured tables.

• Parameters such as column name, line number, and handling of blank lines allow for precise data extraction.

• The rule is useful when you need to extract specific values from tables in a structured document.

Note:

• The "Extract Anchor Value from All Pages" rule streamlines the extraction of data across multiple pages of a document table.

• The use of an anchor field as a reference point enhances the precision of data extraction.

• This rule is valuable when you need to collect specific values from a structured table spanning multiple document pages.

Note:

• The "Filter by Value Index" rule simplifies the process of extracting specific checkbox labels from a checkbox group.

• When used in conjunction with the "Get the Checkbox Value" rule, you can accurately extract and analyze checkbox-related data.

• This rule enhances efficiency when working with forms and documents that feature checkbox groups and associated labels.

Note:

• The "Generate Sequence Number for Table" rule streamlines the process of assigning sequential numbers to table rows.

• This rule enhances data organization and facilitates analysis by providing a clear index for each extracted record.

• Sequential numbering helps maintain the integrity of the data and ensures accurate referencing of individual rows within the table.

Note:

• The "Get All Lines" rule is valuable for capturing contextual information in a structured manner.

• This rule is particularly useful when you need to extract multi-line sections of text based on a reference point.

• The ability to specify the line number and direction allows for precise extraction of relevant content.

Note:

• The "Get Amount in Words" rule is valuable for extracting and converting written monetary amounts into numerical values.

• This rule is commonly used in financial and accounting contexts to ensure accurate calculations and data consistency.

• Post-extraction processing may be required to convert the extracted words into their corresponding numeric values.

Note:

• This rule is specifically designed for extracting data from choice fields represented by checkboxes within tables in unstructured or semi-structured documents.

• It streamlines the selection or deselection of choice field labels based on defined conditions.

• Choice fields should be added as header fields and not under the Details/Tables section of the document.

• The rule introduces the concept of a "search zone" to narrow down the scope of checkbox extraction, which is particularly useful in unstructured or semi-structured documents.

Note:

• The "Get Currency Value" extraction rule is particularly useful when dealing with documents that contain monetary values along with currency codes.

• It allows you to automatically extract currency codes for further processing, eliminating the need for manual data entry.

• The rule is especially valuable in scenarios where large amounts of financial data need to be processed accurately and efficiently.

• The rule can be applied to structured and unstructured documents alike, enhancing data extraction capabilities across various document formats.

Note:

• The "Get Lookup Table Field" extraction rule is particularly useful when dealing with tables containing overlapping values or specific extraction conditions.

• It enables accurate extraction of data from tables, even when values are partially visible or overlap with other cells.

• The rule's parameters allow you to fine-tune the extraction process by specifying word count, position, and synonyms to match the column names.

• The rule contributes to data accuracy and efficiency in structured document processing.

Note:

• The "Get the Maximum Date" extraction rule is particularly useful when dealing with documents containing multiple date values and you need to identify the most recent date.

• The rule streamlines the process of selecting the latest date value without requiring explicit instructions for date comparison.

• It contributes to data accuracy and efficiency in cases where the most recent date value is crucial.

Note:

• The "Get the Minimum Date" extraction rule is particularly useful when dealing with documents containing multiple date values, and you need to identify the earliest date.

• The rule streamlines the process of selecting the earliest date value without requiring explicit instructions for date comparison.

• It contributes to data accuracy and efficiency in cases where the earliest date value is crucial.

Note:

• The "Get Multi Line Text" extraction rule is particularly useful when you need to extract a portion of text that spans multiple lines within a document.

• The rule provides a systematic way to extract specific content based on line numbers and page numbers, reducing the need for manual searching and selection.

• It is especially valuable for extracting structured information from forms and documents that are organized in a consistent manner.

Note:

• The minimum and maximum length properties for the required field (for which Get Multiple Words is being used) should be set.

• The rule expects the data for a given synonym to be lying either towards the right of the synonym or below it.

• The "Get Multiple Words" extraction rule is particularly useful when you need to extract textual content that is not confined to a single line but rather extends across multiple lines.

• This rule is commonly used for capturing descriptions, phrases, sentences, or any other textual content that forms a continuous unit but might be divided across lines in the document.

• By setting the minimum and maximum length properties, you can define the scope of the text to be captured, ensuring accurate extraction of the desired content.

Note:

• The "Get Numeric Values Based on Keyword" extraction rule is particularly useful when you need to extract numeric data that is accompanied by specific keywords within a document.

• This rule helps you capture the numeric value associated with a keyword while taking into account its order sequence relative to other values.

• By selecting the appropriate order sequence (highest or lowest), you can accurately capture the desired numeric value from the document.

• This rule is commonly used in scenarios where data with varying keywords needs to be organized and processed based on their corresponding numeric values.

Note:

• The "Get Numeric Values" extraction rule is versatile and useful when you need to extract numeric values in various formats from a document.

• This rule can handle both whole numbers and decimal values, accommodating different numeric representations.

• By specifying the parameters correctly, you can ensure accurate extraction of numeric values, regardless of their format.

• The rule's flexibility allows you to customize extraction based on the desired decimal places, separator, and inclusion/exclusion of integer values.

• This rule can be applied to scenarios where extracting numeric data is essential, such as financial documents, invoices, receipts, and more.

Note:

• The "Get the Overlapping Value" extraction rule is useful when you need to extract words that are positioned adjacent to or overlapping with a reference word.

• By specifying the parameters correctly, you can ensure that the rule captures the desired word(s) based on the position of the reference word.

• This rule is versatile and can be applied to scenarios where capturing specific words related to a reference word is crucial.

• The rule's flexibility allows you to customize the search direction and span to accurately extract overlapping values.

• This rule can be particularly helpful in situations where the positioning of certain words in relation to reference words is important for data extraction.

Note:

• In the above scenario, the parameter values {rightbottom,Allow} would also work fine.

• Get Single Word rule uses synonyms to find the required data.

• It may happen that multiple data suspects are extracted in regard to the synonyms associated with the required field. In this case, you will may have to validate the extracted values to find the required one.

• The "Get Single Word" extraction rule is useful when you need to extract a single word based on its position in relation to synonyms.

• By specifying the parameters correctly, you can ensure that the rule captures the desired word based on its position and associated synonyms.

• This rule is versatile and can be applied to scenarios where extracting a specific word from a particular position is necessary.

• The rule's flexibility allows you to customize the direction and specify whether to allow extraction when the position doesn't exactly match.

• This rule can be particularly helpful in situations where word positioning in relation to synonyms is important for data extraction.

Note:

• The "Extract Table Field using Other Table Field" extraction rule is useful for extracting structured data from document tables.

• By providing the correct table name, column reference, and optional parameters (such as "Values"), you can ensure accurate data extraction.

• This rule is suitable for scenarios where data is organized in tables and you need to extract specific information from designated columns.

• The rule's flexibility allows you to extract data based on column references and apply conditions to specify which data to ignore or extract.

• Proper parameter setup ensures that you capture the desired values from the specified table and column while accounting for any specified conditions.

• Using this rule, you can automate the process of extracting data from structured tables, saving time and ensuring accuracy in data extraction.

Note:

• The "Get Checkbox Value" extraction rule is designed to extract information from checkbox selections within a document.

• It is particularly useful for scenarios where users have marked checkboxes to indicate their choices.

• Properly defining the search zone using the "Search Zone" parameter ensures that the rule targets the correct area of the document.

• By specifying the page number where the checkboxes are located, you can accurately extract the checkbox labels from the desired page.

• After using the "Get Checkbox Value" rule to extract the checkbox labels, you can further process or filter the extracted labels using additional rules if needed.

• This rule is valuable for automating the extraction of checkbox-based data from documents, saving time and ensuring accurate data capture.

Note:

The List checkbox (under the Properties tab) for the given field should be selected.

Note:

• The "Get List of Values" extraction rule is used to gather a collection of data items from a specified column within a datatable.

• This rule is particularly valuable when you need to extract a list of related values for further processing or analysis.

• Ensure that the "List" checkbox is selected under the Properties tab for the column you want to extract values from.

• The parameter "Name" should be set to the name of the column you are extracting values from. If the list is dynamic, use the column name from the query.

• The parameter "Value" should be a reference to the datatable containing the list of values you want to extract.

• The "Get List of Values" rule is commonly used in scenarios involving data collection, data analysis, and data manipulation.

• Extracting lists of related values from datatables is essential for streamlining processes that involve working with collections of data items.

Note:

• The "Get Passport Values from Field with Type MRZ Data" extraction rule is used to extract specific information from the Machine Readable Zone (MRZ) of a passport document.

• The MRZ contains essential personal data of the passport holder, and this rule allows you to extract specific pieces of information for various purposes.

• The "Passport Field" parameter determines the type of data you want to extract, such as "Date of Birth," "Passport Number," and more.

• The "Field" parameter should be a reference to the MRZ field from which you want to extract the data.

• This rule is particularly valuable in scenarios where you need to quickly capture and utilize specific data points from passport documents.

• Extracting data from the MRZ helps streamline identity verification, data entry, and other processes that involve utilizing passport information.

Note:

• The "Get Passport Values from Line" extraction rule is used to extract specific information from the Machine Readable Zone (MRZ) of a passport document.

• The MRZ contains essential personal data of the passport holder, and this rule allows you to extract specific pieces of information for various purposes.

• The "Passport Field" parameter determines the type of data you want to extract, such as "Country Name," "Date of Birth," "Passport Number," and more.

• This rule is particularly valuable in scenarios where you need to quickly capture and utilize specific data points from passport documents.

• Extracting data from the MRZ helps streamline identity verification, data entry, and other processes that involve utilizing passport information.

Note:

• The "Extract Email Value from Input Source" extraction rule is used to extract specific information from the content of an email.

• This rule can be valuable in scenarios where you receive a large volume of emails and need to automatically extract key information for processing.

• The "Value" parameter determines the type of information you want to extract, such as "Email Body," "Sender Name," "Subject," and more.

• Extracting data from emails helps streamline various tasks that involve processing email information, such as categorization, analysis, and data entry.

• By utilizing the "Get value from the Email" extraction rule, you can automate the extraction of relevant data from emails and enhance efficiency in managing email communications and associated tasks.

Note:

The extracted word(s) along with the reference word are returned as a single string by the rule.

Note:

• The "Get Words on Line" extraction rule is particularly useful when you need to capture words positioned relative to a reference word within a line of text.

• This rule helps in cases where words are separated by spaces or other characters and need to be accurately captured for further processing.

• The "Word" parameter allows you to specify whether you want to extract all words, the last word, or neighboring words.

• The "Value" parameter determines the direction from which the word(s) will be extracted, either to the left or right of the reference word.

• By utilizing the "Get Words on Line" extraction rule, you can ensure accurate extraction of specific words from lines of text in your documents, helping to improve data accuracy and subsequent analysis.

Note:

• The "Get Word Count" extraction rule is particularly useful when you want to quantify the frequency of a specific word on a particular line in a document.

• This rule helps in scenarios where you need to track how many times a certain word appears in a specific context or section.

• The "Text" parameter allows you to specify the word for which you want to calculate the count. The word must be prefixed with "@".

• The "Name" parameter refers to a reference field or string indicating the position where the count of the specified word is to be found.

• By utilizing the "Get Word Count" extraction rule, you can accurately determine the number of occurrences of a specific word, aiding in data analysis and decision-making processes.

Note:

• The "Get Words on the Line" extraction rule is valuable when you need to capture specific information located on a certain line in relation to an anchor text.

• This rule helps in scenarios where the target information is located on a particular line and can be accurately located using a nearby anchor text.

• Parameters like "Number" and "Specific" help you define the search scope and direction, allowing flexibility in adapting to various document layouts.

• By utilizing the "Get Words on the Line" extraction rule, you can ensure accurate data extraction even when the target information's exact position may vary across different documents.

Note:

• The "Get Multi Word Value Columnar" extraction rule is useful when you need to capture a block of text located between two specified points in a document.

• This rule allows you to define the extraction range using stop words as references, providing precise control over the content you want to capture.

• By utilizing the "Get Multi Word Value Columnar" extraction rule, you can ensure accurate extraction of continuous text sequences, such as paragraphs, descriptions, or any other content present between specific points in the document.

Note:

• This rule is used for extracting data from the table section of a document. To extract data from the header and footer section of the document, use Lookup from DataTable extraction rule.

• A derived field cannot be set as a splitter field, as the splitter field toggle switch is disabled for the user.

• It's particularly useful for retrieving additional information from a database or datatable based on values present in the document's table.

• By setting up the parameters correctly, you can establish a link between data in the document and external data sources, enhancing the extracted information's accuracy and context.

Note:

• This rule is used for extracting data from the header and footer sections of a document. To extract data from the table section of the document, use Lookup from DataTable (for Table Fields) extraction rule.

• A derived field cannot be set as a splitter field, as the splitter field toggle switch is disabled for the user.

• It is particularly useful when you need to retrieve additional information from a datatable based on values present in the document.

• By setting up the parameters correctly, you can establish a link between data in the document and external data sources, enhancing the extracted information's accuracy and context.

• This rule can be used to populate derived fields with valuable information that might not be directly extractable from the document itself.

Note:

• This rule is particularly useful when you have a large datatable extracted from a document and want to filter out specific rows that match certain criteria.

• By providing the keyword or synonym to be removed as a parameter, you can ensure that only the relevant data remains in the datatable.

• The "Remove Row from the Table" rule aids in creating a cleaner and more concise dataset by excluding rows that don't contribute to the desired information.

• After applying this rule, the resulting datatable will only include the rows that meet your criteria and exclude the rows with the specified keyword or synonym.

Note:

• This rule is especially useful when you want to extract specific information located within a known region on a page, such as header sections, tables, or footer sections.

• By narrowing down the search area using the horizontal and vertical edges along with the percentage values, you can achieve precise and targeted extraction.

• The "Search in Page Zone" rule is an effective way to improve extraction accuracy by ignoring irrelevant parts of the page and focusing on the desired content.

• It's important to determine the appropriate boundaries and percentages based on the layout of your documents to ensure accurate extraction of information.

Note:

• This rule is particularly useful when you want to extract information from a specific area that remains consistent across different pages of a document.

• By defining the search area and applying it to a list of pages, you can efficiently extract information from a set of pages in a standardized manner.

• The "Search in Page Zone using Page List" rule is effective for situations where the layout of the document ensures that relevant information is consistently placed within a specific region on each page.

• It's important to determine the appropriate boundaries and percentages based on the layout of your documents to ensure accurate extraction of information from the specified region across multiple pages.

Note:

• This rule is useful when you need to extract information located on a specific line with reference to an anchor word.

• The "Search on a Line from Anchor Text" rule allows you to narrow down the search to a specific line and define the area within that line where you expect to find the required information.

• It's important to carefully define the line distance and horizontal search span to accurately locate and extract the desired information.

• The rule is effective for situations where the layout of the document ensures that relevant information is consistently placed on a particular line in relation to an anchor word.

• By specifying multiple anchor words, you can search for information across multiple reference points, increasing the flexibility of the rule in various scenarios.

Note:

• This rule is particularly useful when you need to extract information related to different keywords located on the same line or slightly below the anchor words.

• You can use multiple anchor words to search for different pieces of information within the same search span.

• The "Search on a Line from Multiple Anchor Text" rule allows for flexibility in capturing data that may be scattered across a specific region on a line, making it suitable for cases where data may appear in various positions.

• Careful selection of anchor words and accurate definition of the search span are essential to ensure accurate extraction.

• This rule can improve efficiency in scenarios where multiple pieces of related information need to be extracted from the same region on a line.

Note:

• This rule is particularly useful when you want to handle scenarios where not all keywords may be present, and you want to decide whether to discard the extraction attempt or retain it with a fallback strategy.

• The "Action on Fail" option provides flexibility in managing extraction attempts when the expected data is not found.

• Careful selection of anchor words, accurate definition of the search span, and appropriate choice of the action on fail are essential to ensure the desired extraction behavior.

• This rule allows for more controlled handling of extraction results, enhancing the accuracy and quality of data extraction processes.

Note:

• Careful selection of the anchor text, accurate definition of the search span, and proper choice of search area and direction are crucial for successful extraction.

• The "Filter Using Nearest Word" rule is especially useful when you need to extract data that is positioned at a specific distance from an anchor text, considering both vertical and horizontal offsets.

• This rule enhances the accuracy and flexibility of data extraction processes, allowing you to target specific data points efficiently.

Note:

In case multiple instances of data are found to be matching the regular expression, they all will get fetched.

Note:

• Regular expressions are a versatile tool for pattern matching in text. They can be as simple or complex as required, and they allow you to define intricate patterns for data extraction.

• This rule is particularly useful when the data you need to extract follows a specific pattern that can be effectively described using regular expressions.

• Regular expressions can be used to match various types of data, such as email addresses, phone numbers, dates, and more, based on the patterns they adhere to.

• Careful crafting of the regular expression pattern is essential for accurate data extraction. Regular expressions can be complex, so testing and refining the pattern may be necessary to achieve the desired results.

Note:

• You can apply regular expressions from the Regular Expressions tab available in the ontology window. Also, if multiple instances of data are found to be matching the regular expression, they all will get fetched.

• Regular expressions are versatile tools for pattern matching in text. They provide a flexible and powerful way to extract data that follows specific patterns.

• The Regular Expressions tab in the ontology window allows you to define and manage regular expressions for various extraction tasks.

• Regular expressions can be used to match a wide range of data patterns, such as email addresses, dates, URLs, and more.

• Proper understanding and construction of regular expression patterns are crucial for accurate data extraction.

• If multiple instances of data match the specified regular expression, this rule will fetch all matching instances.

• Regular expressions can be complex, so testing and refining the pattern may be necessary to achieve accurate and desired results.

Note:

• This rule is especially useful for structured documents where data is presented in tables, such as financial reports, invoices, and forms.

• Ensure that the anchor field is accurately identified to correctly reference the starting point for data extraction.

• The direction parameter ("Top" or "Bottom") determines whether the search is conducted upwards or downwards from the anchor field.

• The extracted data will be the value found in the table field at the specified direction from the anchor field.

Note:

• This rule is valuable when working with tabular data that contains rows that need to be excluded from analysis or extraction.

• Ensure that the start and end words are accurately identified to define the range of rows you want to skip.

• The rows within the specified range will be skipped, and the extraction will continue with the remaining rows.

• This rule can help streamline data extraction by ignoring irrelevant or unnecessary rows.

Note:

• This rule is particularly useful for documents with tables where relevant information is embedded within descriptive text.

• Ensure that the synonyms and separator are accurately defined to extract the desired values effectively.

• This rule improves data extraction efficiency by targeting specific values within descriptive fields.

• Use this rule when you want to extract structured information from textual descriptions within a table.

Note:

• Ensure that you provide the correct column name and table index to accurately calculate the sum.

• This rule is valuable for extracting aggregated numerical data, such as total sales, expenses, or profit/loss figures, from structured tables.

• Utilize this rule when you want to automate the process of summing values across multiple rows in a specific column.

Note:

• Ensure that you provide the correct table name and position (above or below) to accurately extract the desired value.

• This rule is particularly useful when you have important contextual information, such as reference numbers or notes, that are positioned relative to specific tables in your document.

Note:

• Ensure that you provide the accurate column header names for the left and right sides to extract data from the intended column.

• This rule is particularly handy when you need to work with data in a table where some columns might be missing their headers.

Note:

• Ensure that you correctly specify the values you want to restrict from extraction.

• This rule is particularly useful when you want to ensure accuracy and quality control by excluding unwanted or irrelevant values from your automated extraction results.

Note:

• Ensure that the reference word and synonyms are correctly identified for accurate extraction.

• This rule can be particularly helpful when you need to extract specific values that are positioned relative to other reference words in a document.

Note:

• Ensure that the context of the document and the extracted dates align with the intended use of the rule.

• This rule is particularly useful when you have date values with incomplete information and need to standardize them by adding missing components.

Note:

• Ensure that the address field in the document is properly recognized and segmented so that the rule can accurately extract the desired information.

• Verify that the chosen parameter values align with the information you are seeking to extract from the address field.

Note:

• Ensure that the keywords on the X and Y axes, as well as the anchor keyword, are accurately recognized within the document or dataset.

• Verify that the chosen keywords accurately represent the intersection point for the value you are seeking to extract.

Note:

• Ensure that the rule is applied correctly to the desired table and that the description text you want to extract overlaps with other columns.

• Verify that the rule accurately identifies and extracts the extra description text without including any unrelated content.

Note:

• The "Check Character Case" validation rule is useful for ensuring consistent data formatting, particularly when character case conformity is crucial.

• It helps maintain data accuracy and quality by flagging or correcting extracted values that do not meet the defined character case expectations.

Note:

• The "Check if Alphabets" validation rule is useful for verifying the data quality and ensuring that certain fields, such as names or labels, contain only valid characters.

• Empty spaces in the extracted data are ignored.

• This rule can be used to prevent incorrect or invalid data from being included in further processing or analysis.

Note:

• The "Check if AlphaNumeric" validation rule is useful for verifying the validity of data that should be a combination of letters and digits, such as identification numbers, codes, and references.

• Blank spaces are ignored.

• This rule helps prevent incorrect or invalid data from being included in further processing or analysis.

Note:

• The "Check if Dependent Field" validation rule is particularly useful when there is a logical relationship between different fields in a document, and the extraction of one field is dependent on the successful extraction of another.

• This rule helps maintain data integrity by ensuring that related fields are consistently and correctly extracted together.

• Proper parameter configuration is important to accurately validate field dependencies and manage the actions to be taken when validation fails.

Note:

• The "Check if Value is Numeric" validation rule is useful when you want to ensure that a field contains a valid integer value and doesn't include any non-numeric characters.

• This rule is especially relevant for fields that are expected to represent numerical values, such as pin codes, IDs, quantities, etc.

• While the rule considers negative integer values as valid, it will fail if the data contains non-numeric characters or decimal points.

• Note:

• The "Check Character Limit Length" validation rule is used to ensure that the length of the extracted data falls within a specified range.

• This rule is particularly relevant when you want to enforce length constraints on fields, such as phone numbers, codes, or identifiers.

• By setting the minimum and maximum length, you can define the acceptable range for the length of the extracted data.

• The rule considers the lower and higher limits to be inclusive in the specified range.

• If the data length does not match the specified range, the action specified in the "Action on Fail" parameter will be executed.

Note:

• The "Check Currency Value" validation rule ensures that extracted currency values adhere to a specified currency style format.

• This rule is useful for maintaining consistency and accuracy in currency values extracted from various documents.

• By specifying the currency style or using the "AutoDetectCurrency" option, you can validate that the currency values match the expected format.

• If the data doesn't match the currency style format, the action specified in the "Action on Fail" parameter will be executed. This can involve discarding the data or proceeding with the extraction process based on your requirements.

Note:

• The "Check Occurrence of Digit/Letter" validation rule is used to ensure that the occurrence count of a specific data type (digit or letter) in the extracted data meets certain criteria.

• All physical dimensions will have at least three integers, each representing length, breadth, height of the product respectively. The least value of each dimension can be one, which is a single digit integer. Since there are three dimensions, the minimum count of digits in the extracted data should be three.

• This rule is particularly useful when you need to validate that the extracted data contains a minimum number of digits or letters to fulfill specific requirements.

• By setting the appropriate value and choosing the data type (digit or letter), you can ensure that the extracted data meets the occurrence criteria.

• If the data doesn't meet the specified occurrence criteria, the action specified in the "Action on Fail" parameter will be executed. This can involve discarding the data or proceeding with the extraction process based on your requirements.

Note:

• The "Compare Amount in Words" validation rule is used to verify the accuracy of text-based representations of numeric values (amount in words) by comparing them with actual numeric values.

• This rule ensures that the amount in words matches the numeric value, providing a measure of validation for financial or numerical data.

• By comparing the numeric value to the extracted amount in words, you can identify inconsistencies or errors in the data.

• The "Action on Fail" parameter determines how the rule responds if a mismatch is detected. You can choose to discard the data or retain it, based on your requirements.

Note:

• The "Compare Date" validation rule is used to enforce date-based comparison checks during data validation.

• You can use this rule to ensure that dates meet certain conditions, such as being greater than or equal to a specific reference date.

• The "Action on Fail" parameter determines how the rule responds if the comparison condition is not met. You can choose to discard the data or retain it based on your requirements.

Note:

• The "Compare Lookup Values" validation rule is used to verify if extracted data matches a reference value.

• You can apply this rule to ensure that specific data elements, such as identification numbers or codes, match expected values.

• The "Action on Fail" parameter determines the rule's behavior when the validation fails. You can choose to discard the data or retain it based on your requirements.

Note:

• This rule is useful for verifying if an extracted data value is present in a reference datatable.

• The "Type" parameter specifies whether an exact match or a near match should be considered. As of now, "Near Match" is reserved for future use.

• The "Threshold" parameter defines the similarity threshold percentage for a match. The available options are 100% (exact match) and NA (Not Applicable).

• The "Action on Fail" parameter determines the rule's behavior when the validation fails. You can choose to discard the data or retain it based on your requirements.

Note:

• This rule is useful for identifying and handling cases where the extracted data might contain keywords that indicate the data is not valid or relevant.

• By specifying a list of invalid keywords, you can prevent incorrect or irrelevant data from being processed further.

• The Action on Fail parameter allows you to define how the rule should respond when the validation check fails, ensuring the accuracy of your data processing pipeline.

Note:

• This rule is essential when you want to ensure that extracted data conforms to a standardized format, in this case, the format of Aadhaar numbers.

• The rule does not verify whether the Aadhaar number is actually valid or exists in the UIDAI's database. It only confirms whether the data follows the UIDAI's specified format.

• Use this rule when dealing with Aadhaar-related data to ensure data quality and adherence to the defined format.

Note:

• This rule is useful for maintaining data quality when dealing with monetary figures and financial data.

• The validation check focuses on the format of the data and whether it resembles a valid monetary amount. It does not perform actual calculations or currency conversion.

• Use this rule to ensure that monetary amounts extracted from various documents adhere to a consistent and standardized format.

Note:

• Standard date format can be any combination day, month, and year separated by a space or any character.

• Use this rule to maintain data integrity and quality when dealing with date information.

• The validation check focuses on the structure of the date representation and whether it follows common date format conventions.

• The rule does not perform actual date validation (e.g., checking for valid days in a month) or conversion.

• Adjust the Action on Fail parameter to determine how the validation rule handles data that fails to match the standard date format.

Note:

• Use this rule to ensure that email addresses extracted from documents adhere to recognized email address patterns.

• The validation check focuses on the structure of the email address representation and whether it follows common email address format conventions.

• The rule does not verify the actual existence or validity of the email address with an email server.

• Adjust the Action on Fail parameter to determine how the validation rule handles data that fails to match the valid email address format.

Note:

• Use this rule to ensure that GST numbers extracted from documents adhere to the format prescribed by the GST Council of India.

• The validation check focuses on the structure of the GST number representation and whether it follows the GST Council's conventions for valid GST numbers.

• The rule does not verify the actual existence or validity of the GST number with the GST portal.

• Adjust the Action on Fail parameter to determine how the validation rule handles data that fails to match the valid GST number format.

Note:

• Use this rule to validate that extracted data representing numeric amounts is in a valid format.

• The validation check includes both integer values and numeric values up to two decimal places.

• The rule doesn't validate the correctness or accuracy of the numeric value; it focuses solely on the format.

• Customize the Action on Fail parameter to define how the validation rule handles data that does not match the valid numeric amount format.

Note:

• Use this rule when you want to ensure that the extracted data follows a specific pattern that includes both numeric and special characters.

• The rule allows you to set the minimum counts of numeric characters and special characters required in the data.

• Depending on the validation result, you can choose to retain, discard, or take other actions on the extracted data using the Action on Fail parameter.

• Ensure that the criteria set for numeric and special character counts align with your specific validation requirements.

• Accurate extraction and document field configuration are essential for correct validation.

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• Use this rule to validate that extracted data representing IFSC codes is in a valid format according to the standards set for Indian Financial System Codes.

• The validation check is focused on the format of the IFSC code, not on its existence or accuracy.

• Customize the Action on Fail parameter to determine how the validation rule handles data that does not match the valid IFSC code format.

Note:

• Use this rule to validate that extracted data representing PAN numbers is in a valid format according to the standards set for Permanent Account Numbers.

• The validation check focuses on the format of the PAN number, not on its existence or accuracy.

• Customize the Action on Fail parameter to determine how the validation rule handles data that does not match the valid PAN format.

Note:

• Use this rule to validate that extracted data follows a specific sequence pattern, such as a particular format for invoice numbers, order numbers, or any other structured sequence.

• Customize the Action on Fail parameter to determine how the validation rule handles data that does not match the specified sequence pattern.

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• Use this rule to ensure that characters extracted from the data represent a valid telephone number format, including digits and specific characters like '-', ')', and '('.

• Customize the Action on Fail parameter to determine how the validation rule handles data that contains characters not suitable for a valid telephone number description.

Note:

• Use this rule to ensure that characters extracted from the data represent a valid telephone number format, including digits and specific characters like '>', '+', '-', ')', '(', and ' ' (space).

• Customize the Action on Fail parameter to determine how the validation rule handles data that does not adhere to a valid telephone number description.

Note:

• The lower and higher limits are inclusive.

• Use this rule to ensure that extracted numeric values fall within a defined range.

• The validation is inclusive, meaning that data equal to the minimum or maximum values is considered valid.

• Customize the Action on Fail parameter to determine how the validation rule handles data that is outside the specified range.

Note:

• Use this rule to validate whether extracted cell data matches values in a datatable.

• The near match threshold allows for minor discrepancies in the values.

• Customize the Action on Fail parameter to determine how the validation rule handles data that does not meet the validation criteria.

Note:

The address will be considered for 3 lines from above the pincode. The total address line would be 4 lines.

Note:

• This rule checks whether the extracted data resembles a valid US address format and does not verify the actual existence of the address.

• Use this rule to ensure that the extracted addresses are in the correct US address format. Customize the Action on Fail parameter to determine how the validation rule handles data that does not meet the validation criteria.

Note:

• This rule checks whether the extracted data resembles a valid UK telephone number format and does not verify the actual existence of the phone number.

• Use this rule to ensure that the extracted phone numbers are in the correct UK phone number format. Customize the Action on Fail parameter to determine how the validation rule handles data that does not meet the validation criteria.

Note:

• This rule checks whether the extracted data resembles a valid US telephone number format and does not verify the actual existence of the phone number.

• Use this rule to ensure that the extracted phone numbers are in the correct US phone number format. Customize the Action on Fail parameter to determine how the validation rule handles data that does not meet the validation criteria.

Note:

• Regular expressions allow you to define complex patterns for data validation.

• Use this rule when you need to validate the extracted data against a specific pattern that cannot be validated using other predefined rules.

• The Custom Pattern parameter should be written in a valid regular expression syntax.

• Depending on whether the data matches the custom pattern or not, you can decide whether to discard, retain, or take other actions on the data using the Action on Fail parameter.

Note:

• Use this rule when you want to ensure that the extracted data represents valid web addresses (URLs).

• The rule does not verify whether the extracted web address is an existing or reachable URL; it only checks if the format is correct.

• Depending on whether the data follows a valid web address format or not, you can decide whether to discard, retain, or take other actions on the data using the Action on Fail parameter.

• The rule considers various URL formats, including those starting with "http://," "https://," and "www."

Note:

• Use this rule when you want to ensure that the extracted data represents zip codes in the correct format for a specific country.

• The rule validates the format of the extracted data against the zip code format associated with the provided country code.

• Depending on whether the extracted data follows a valid zip code format or not, you can decide whether to discard, retain, or take other actions on the data using the Action on Fail parameter.

• The rule does not verify the actual validity of the zip code itself; it only checks whether the format is correct for the specified country.

Note

• This rule is useful for ensuring data accuracy and quality by validating extracted information against predefined standards or conditions.

• The rule allows you to implement data quality control and prevent incorrect or irrelevant data from being retained.

• The ability to discard or retain data based on the validation outcome provides flexibility in managing data extraction results.

Note:

• In case the extracted value would have been, say, 8,23 percent, and the Rounding Type would have been set to Roundup, then based on the above parameters, the extracted value would have become 8,3.

• Ensure that the parameter values are accurately set based on your formatting preferences.

• Verify that the rule behaves as expected when applied to various numeric values and rounding scenarios.

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• Ensure that the delimiter and index values are set correctly to match the structure of your delimited data.

• Verify the rule's behavior with various input scenarios to ensure accurate extraction.

Note:

• Ensure that the sequence(s) and the place parameters are set correctly to target the characters you want to remove and the position from which they should be removed.

• Depending on your data, you can use this rule to remove unwanted characters, spaces, specific patterns, and more.

• The rule should be placed in the extraction sequence accordingly, so it receives the extracted data as input.