Description:AI-POWERED SYSTEM AND METHOD FOR AUTOMATED MULTILINGUAL DOCUMENT LOCALIZATION WITH PRESERVED LAYOUT AND FORMATTING
FILED OF THE INVETION
[001] The present invention relates to the field of document processing and, more specifically, to systems and methods for automating multilingual document localization while preserving document structure and formatting.
BACKGROUND OF THE INVENTION
[002] Background description includes information that may be useful in understanding the present invention. It is an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[003] Traditional document localization involves several manual steps, which can be slow, error-prone, and expensive. These steps usually include extracting text from the original document, translating it into another language, and then adjusting the layout to match the original format. This process becomes even harder when dealing with complex documents that have tables, images, or special fonts. Since people do most of the work, there is a higher chance of mistakes, inconsistencies, and delays, especially for large document batches or multiple languages.

[004] Many existing translation tools do not fully solve these issues. They mainly focus on text translation but ignore the document’s layout. As a result, the translated document may have misaligned text, distorted images, or broken formatting, making it harder to read and understand. Additionally, traditional translation methods often struggle to maintain the original meaning, tone, and style, especially in specialized fields like law, medicine, or technical content.

[005] There is a strong need for a better, automated solution. An ideal system should accurately extract text from different document types, provide accurate and context-aware translations, and preserve the original layout. It should also be fast, scalable, and flexible to meet different user needs. A well-designed solution can simplify document localization, improve accuracy, and maintain consistency across translated documents.

OBJECTIVES OF THE INVENTION
[006] The present invention aims to address the limitations of conventional document localization methods by providing a system and method that leverages artificial intelligence (AI) and advanced language processing techniques to automate and streamline the localization workflow.
[007] One objective of the invention is to provide a system that accurately extracts text from documents in various languages and formats, including handwritten documents, using AI-powered OCR and contextual error correction.
[008] Another objective is to provide a system that accurately translates the extracted text into a target language while preserving the semantic meaning, tone, context, and intent of the original content.
[009] Yet another objective is to provide a system that preserves the original document's layout, formatting, and visual elements during the translation process, ensuring that the localized document is visually and functionally equivalent to the source document.
[010] A further objective is to provide a system that dynamically adjusts the translated text layout to accommodate language-specific variations, such as text expansion or contraction, preventing truncation and misalignment issues.
[011] An additional objective is to provide a system that enables real-time document localization, allowing instant text extraction, translation, and reformatting as users interact with the document.
[012] A further objective is to provide a system that incorporates user feedback to iteratively refine translation and layout preservation, adapting to specific preferences and nuances.
[013] Yet another objective is to provide a system that preserves metadata during the localization process, ensuring document integrity and compliance with regulatory requirements.
[014] A further objective is to provide a system that integrates seamlessly with enterprise workflows and external applications, facilitating efficient and automated document localization within various business environments.

SUMMARY OF THE INVENTION

[015] In accordance with an embodiment, the present disclosure provides a system for automating multilingual document localization. The system comprises an input unit configured to receive an original document, an AI-enhanced Optical Character Recognition (OCR) module configured to extract text from the original document and apply contextual error correction to rectify spelling and grammatical mistakes, a translation module configured to translate the extracted text into a target language while preserving the semantic meaning, tone, context, and intent of the original content, a document layout preservation engine configured to maintain the original document's structure and formatting, including font styles, text alignment, tables, images, and other complex elements, a dynamic text resizing module configured to adjust text positioning, font sizes, and layout spacing based on language-specific expansion or contraction rates to ensure translated text conforms to the original document's spatial constraints, and an output unit configured to provide a localized document that has been translated and formatted to match the original document's layout.
[016] In accordance with an aspect, the AI-enhanced OCR module utilizes a deep learning model for improved text extraction accuracy across printed and handwritten documents.
[017] In accordance with an aspect, the system further comprises a user-assisted feedback module configured to iteratively refine translation and layout preservation based on real-time user input, wherein a reinforcement learning engine adapts dynamically to user preferences and corrections.
[018] In accordance with an aspect, the translation module applies domain-adaptive semantic mapping to ensure terminology consistency in technical, medical, legal, and cultural documents.
[019] In accordance with an aspect, the system further comprises a metadata preservation engine configured to ensure synchronization and translation of metadata across languages, maintaining version integrity and facilitating compliance with regulatory requirements.
[020] In accordance with an aspect, the AI-enhanced OCR module supports both digital and handwritten text recognition, employing deep learning models trained on diverse handwriting styles to improve extraction accuracy.
[021] In accordance with an aspect, the system further comprises an AI-driven document optimization module configured to analyze the complexity of source documents and suggest improvements to enhance localization efficiency.
[022] In accordance with an embodiment, the present disclosure provides a method for document localization, comprising the steps of: pre-processing a document by enhancing the image quality to optimize text extraction accuracy; extracting text from the document and applying contextual error correction; translating the corrected text into a target language while preserving semantic intent, domain-specific terminology, and cultural nuances; maintaining document formatting by preserving font styles, text alignment, spacing, and embedded graphical elements during translation; and dynamically adjusting the translated text layout to fit within the original document constraints.
[023] In accordance with an aspect, the method further comprises the step of receiving real-time user feedback and adapting the translation and layout preservation based on said feedback.
[024] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components
BRIEF DESCRIPTION OF THE FIGURES
[025] The accompanying drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[026] FIG. 1 is a block diagram illustrating a system, according to some embodiments of the present disclosure.
[027] FIG. 2 is a flowchart illustrating a method, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[028] The following is a detailed description of embodiments of the invention depicted in the accompanying drawings. The embodiments are in such details as to clearly communicate the invention. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.

[029] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.

[030] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this invention will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).

[031] Various terms as used herein. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.

[032] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[033] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

[034] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.

[035] FIG. 1 is a block diagram that describes a system 100 for automating multilingual document localization, according to some embodiments of the present disclosure. In one embodiment, the system 100 may include an input document (101), an AI-enhanced Optical Character Recognition (OCR) module (102), a translation module (103), a document layout preservation engine (104), a dynamic text resizing module (105), and an output localized document (110).

[036] The AI-enhanced OCR module (102) may comprise a deep learning model (102a) and a contextual error correction processor (102b). The deep learning model (102a) may be configured to extract text from the input document (101) in various languages. The contextual error correction processor (102b) may be configured to rectify spelling and grammatical mistakes in the extracted text.

[037] The translation module (103) may comprise a neural machine translation engine (103a) and a semantic preservation unit (103b). The neural machine translation engine (103a) may be configured to translate the extracted text into a target language. The semantic preservation unit (103b) may be configured to preserve the semantic meaning, tone, context, and intent of the original content during translation.

[038] The document layout preservation engine (104) may comprise a formatting analysis unit (104a) and a layout adjustment unit (104b). The formatting analysis unit (104a) may be configured to analyze the formatting of the input document (101). The layout adjustment unit (104b) may be configured to adjust the layout of the translated text to match the formatting of the input document (101).

[039] The dynamic text resizing module (105) may comprise a text positioning unit (105a) and a font size adjustment unit (105b). The text positioning unit (105a) may be configured to adjust the positioning of text in the translated document. The font size adjustment unit (105b) may be configured to adjust the font size of text in the translated document to ensure that the translated text fits within the layout of the original document.

[040] The AI-enhanced OCR module (102) plays a crucial role in extracting text from the input document (101), regardless of the language or format. This module utilizes a deep learning model, which could be a Convolutional Neural Network (CNN) or a Transformer-based architecture, to accurately identify and extract text, even from handwritten documents. The module also incorporates a contextual error correction processor (102b) that rectifies spelling and grammatical errors, ensuring the extracted text is clean and ready for translation. This advanced OCR module supports both digital and handwritten text recognition, employing models trained on diverse handwriting styles to handle various document types effectively.

[041] The translation module (103) is responsible for accurately translating the extracted text into the desired target language. This module goes beyond simple word-for-word translation by employing a neural machine translation engine (103a) that considers the context and intent of the original content. Additionally, a semantic preservation unit (103b) ensures that the translated text retains the original meaning, tone, and style. To further enhance accuracy, the translation module can leverage domain-adaptive semantic mapping, utilizing knowledge graphs and contextual embeddings to maintain consistency in terminology, especially in specialized fields like technology, medicine, law, and culture.

[042] The document layout preservation engine (104) addresses the critical aspect of maintaining the original document's structure and visual presentation. This engine analyzes the formatting of the input document (101), including font styles, text alignment, tables, images, and other elements. It then dynamically adjusts the translated content to fit within the original layout, resizing text boxes, repositioning elements, and modifying content flow as needed. This ensures that the localized document is not only accurate in translation but also visually consistent with the source document, preserving its professional appearance and readability.

[043] The dynamic text resizing module (105) works in conjunction with the layout preservation engine to handle the challenges posed by text expansion or contraction during translation. Different languages have varying space requirements, and this module intelligently adjusts text positioning and font sizes to accommodate these differences. It employs AI-based heuristics to predict the optimal text placement and font adjustments, preventing issues like text truncation or misalignment, which could disrupt the document's visual integrity.

[044] The system 100 can incorporate a real-time processing module (106) to provide immediate localization as users interact with the document. This module enables dynamic text extraction, translation, and reformatting, allowing users to see the localized content instantly as they upload or modify the source document. This real-time capability is particularly useful for collaborative environments or situations where immediate access to translated content is crucial.

[045] The system 100 can include a user-assisted feedback module (107) to further refine the localization process. This module allows users to provide feedback on the translation and layout, which is then used to iteratively improve the system's performance. By incorporating user feedback, the system can adapt to specific preferences and nuances, ensuring greater accuracy and user satisfaction.

[046] The system 100 may include a metadata preservation engine (108) to manage the metadata associated with the document. This engine ensures that metadata, such as author information, creation date, and keywords, is synchronized and translated along with the document content. This feature is essential for maintaining document integrity, facilitating efficient information management, and ensuring compliance with regulatory requirements.

[047] The system 100 can incorporate an AI-driven document optimization module (109) to enhance the overall localization process. This module analyzes the complexity of the source document and suggests improvements to enhance localization efficiency. It can recommend layout changes, alternative phrasings, or restructuring to improve readability and make the translation process smoother.

[048] FIG. 2 is a flowchart illustrating a method 200 for document localization, according to some embodiments of the present disclosure. In another embodiment, the method 200 may include receiving an input document (202), preprocessing the input document (203), performing Optical Character Recognition (OCR) and error correction (204), translating the document (205), preserving the document layout (206), and outputting a localized document (211).

[049] The step of pre-processing the input document (203) may involve enhancing the image quality of the input document (202) to optimize text extraction accuracy.

[050] The step of performing OCR and error correction (204) may involve extracting text from the input document (202) and applying contextual error correction using an AI-powered OCR engine and natural language processing (NLP) models.

[051] The step of translating the document (205) may involve translating the corrected text into a target language while preserving semantic intent, domain-specific terminology, and cultural nuances.

[052] The step of preserving the document layout (206) may involve maintaining document formatting by preserving font styles, text alignment, spacing, and embedded graphical elements during translation. Preserving the document layout (206) may also involve dynamically adjusting the translated text layout to fit within the original document constraints, ensuring compatibility with different text expansion or contraction rates.

[053] In another embodiment, the method 200 proceeds with Optical Character Recognition (OCR) and error correction (204). In this step, an AI-powered OCR engine extracts text from the preprocessed document, accurately identifying characters and words regardless of the language or writing style. The extracted text then undergoes contextual error correction using natural language processing (NLP) models, which identify and rectify spelling and grammatical errors, ensuring the accuracy and fluency of the translated text.

[054] The method 200 then performs the translation (205) of the corrected text into the desired target language. This translation process utilizes advanced techniques to preserve the semantic intent, domain-specific terminology, and cultural nuances of the original content. The translation engine considers the context and meaning of the text, ensuring that the translated document accurately reflects the original message and tone.

[055] The method 200 of the present invention addresses the crucial aspect of document layout preservation (206). This step involves maintaining the original document's formatting, including font styles, text alignment, spacing, and the positioning of embedded elements such as images and tables. The system dynamically adjusts the translated text layout to fit within the original document's constraints, ensuring compatibility with different text expansion or contraction rates that may occur during translation. This meticulous approach ensures that the localized document is not only accurate in translation but also visually consistent with the source document, preserving its original structure and aesthetic appeal.

[056] In another embodiment, the method 200 may also include optional steps such as real-time processing (207), user feedback and adaptation (208), and document optimization (210). These steps further enhance the localization process by enabling dynamic translation and formatting adjustments, incorporating user feedback to refine accuracy, and optimizing the document for better readability and translation efficiency.

[057] The culmination of the method 200 is the output (211) of a localized document that has been accurately translated and meticulously formatted to match the original document's layout. This localized document is ready for use in the target language, maintaining the integrity and visual appeal of the source document while effectively conveying its information to a new audience.

[058] In another embodiment, the document localization system may be integrated with enterprise workflows, enhancing efficiency and streamlining business processes. This integration can be achieved through seamless connectivity with Enterprise Content Management (ECM) systems, allowing for automated document localization within the enterprise environment. Additionally, a real-time API framework can be provided to enable external applications to access and utilize the document localization system, facilitating automated processing and retrieval of translated documents. This integration capability enhances the versatility and adaptability of the system, making it a valuable tool for businesses operating in multilingual environments.

[059] In another embodiment, the system prioritizes the preservation of document integrity during the localization process. This includes maintaining the tone, maning, and intent of the original document through the use of AI-driven translation modules that utilize natural language processing (NLP) and contextual embeddings. Additionally, the system employs dynamic layout adjustment mechanisms to ensure that any text expansion or contraction due to translation does not disrupt the original document's structure. Furthermore, compliance validation modules are incorporated to check translated content for adherence to regulatory requirements, data protection standards, industry-specific regulations, and accessibility standards. This comprehensive approach ensures that the localized document remains faithful to the original content and adheres to all relevant compliance guidelines.

[060] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprise” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C ….and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

[061] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.

[062] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
TECHNICAL ADVANTAGES

[063] The disclosed embodiments provide a significant advancement in the field of document localization, offering a unique combination of features and functionalities that address the limitations of existing methods.

[064] Enhanced Accuracy:
AI-powered OCR: The use of AI-enhanced OCR with deep learning models and contextual error correction significantly improves text extraction accuracy, especially for complex layouts and handwritten documents, minimizing errors that can affect translation quality.
[065] Contextual Translation: The translation module considers the context, tone, and intent of the original content, resulting in more accurate and natural-sounding translations compared to traditional rule-based or statistical machine translation methods.
[066] Domain-Adaptive Mapping: Leveraging knowledge graphs and contextual embeddings ensures consistent terminology in specialized fields, further enhancing translation accuracy and relevance.
[067] Automation: The system automates the entire document localization workflow, from text extraction and translation to layout preservation and formatting, significantly reducing manual effort and processing time.
[068] Real-time Processing: The real-time capabilities enable immediate translation and formatting adjustments, streamlining workflows and increasing productivity.
a. AI-Driven Optimization: The document optimization module analyzes document complexity and suggests improvements, further enhancing localization efficiency.
[069] Layout Preservation: The system meticulously preserves the original document's layout, formatting, and visual elements, ensuring that the localized document is visually and functionally equivalent to the source document.
[070] Dynamic Text Resizing: Intelligent text resizing and repositioning prevent truncation and misalignment issues, maintaining the document's aesthetic appeal and readability.
[071] Metadata Preservation: The system ensures that metadata is synchronized and translated along with the document content, maintaining document integrity and facilitating compliance.
[072] Enhanced User Experience: User-Assisted Feedback: The system incorporates user feedback to iteratively refine translation and layout, adapting to specific preferences and ensuring greater user satisfaction.
[073] Seamless Integration: Integration with enterprise workflows and external applications through APIs provides a smooth and user-friendly experience.
[074] Increased Accessibility: Multilingual Support: The system handles various languages, making information accessible to a wider audience and breaking down communication barriers.
[075] Handwriting Recognition: Support for handwritten documents increases accessibility for diverse content types and sources.
[076] Compliance Validation: The system checks translated content for regulatory adherence, ensuring compliance with data protection, industry-specific regulations, and accessibility standards.

, Claims:We claim:
1. A system for automating multilingual document localization, comprising:
one or more processors;
a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the system to:
receive an original document (101);
extract text from the original document using an AI-enhanced Optical Character Recognition (OCR) module (102) comprising a deep learning model (102a) and a contextual error correction processor (102b), wherein said OCR module (102) is configured to apply contextual error correction to rectify spelling and grammatical mistakes;
translate the extracted text into a target language using a translation module (103) comprising a neural machine translation engine (103a) and a semantic preservation unit (103b), wherein said translation module (103) is configured to preserve the semantic meaning, tone, context, and intent of the original content;
preserve the original document's structure and formatting using a document layout preservation engine (104) comprising a formatting analysis unit (104a) and a layout adjustment unit (104b); and
adjust text positioning, font sizes, and layout spacing based on language-specific expansion or contraction rates using a dynamic text resizing module (105) comprising a text positioning unit (105a) and a font size adjustment unit (105b), wherein said dynamic text resizing module (105) is configured to ensure translated text conforms to the original document's spatial constraints; and an output unit configured to provide a localized document (110).
2. The system as claimed in claim 1, wherein the AI-enhanced OCR module (102) utilizes a deep learning model selected from the group consisting of Convolutional Neural Networks (CNNs) and Transformer-based architectures.
3. The system as claimed in claim 1, further comprising a user-assisted feedback module (107) comprising a feedback input unit (107a) and a reinforcement learning engine (107b), wherein said user-assisted feedback module (107) is configured to iteratively refine translation and layout preservation based on real-time user input.

4. The system as claimed in claim 1, wherein the translation module (103) applies domain-adaptive semantic mapping by leveraging knowledge graphs and contextual embeddings to ensure terminology consistency in technical, medical, legal, and cultural documents.

5. The system as claimed in claim 1, further comprising a metadata preservation engine (108) configured to ensure synchronization and translation of metadata across languages.

6. The system as claimed in claim 1, wherein the AI-enhanced OCR module (102) supports both digital and handwritten text recognition.

7. The system as claimed in claim 1, further comprising an AI-driven document optimization module (109) configured to analyze the complexity of source documents and suggest improvements to enhance localization efficiency.

8. The system as claimed in claim 1, wherein the document layout preservation engine (104) dynamically adjusts content by resizing text boxes, repositioning elements, and modifying content flow to accommodate language-specific variations.

9. The system as claimed in claim 1, wherein the dynamic text resizing module (105) employs AI-based heuristics to predict the optimal text placement and font adjustments, preventing truncation or misalignment during translation.

10. The system as claimed in claim 1, further comprising a real-time processing module (106) with a dynamic text extraction unit (106a), real-time translation unit (106b), and dynamic reformatting unit (106c), wherein the module enables dynamic document localization by continuously extracting, translating, and reformatting text as content is uploaded or modified, using streaming-based text recognition and translation.

11. The system as claimed in claim 1, further comprising a hybrid LLM architecture with specialized compact models for preserving document structure and larger models for complex semantic transformation, a dynamic model selection unit optimizing efficiency and accuracy based on document complexity, and an ECM integration module for seamless automated localization in enterprise environments.

12. The system as claimed in claim 1, further comprising a real-time API framework with an API endpoint and document processing unit configured for external integration, enabling automated processing, retrieval, and multi-modal localization of text, images, scanned documents, audio, and video.

13. The system as claimed in claim 1, further comprising an AI-driven translation module (103) that utilizes natural language processing (NLP) and contextual embeddings to preserve the tone, meaning, and intent of the original document; a dynamic layout adjustment engine (100) comprising a text resizing unit (102), a spacing modification unit (104), and a content repositioning unit (106), wherein the engine automatically adjusts text size, spacing, and positioning to accommodate translation-induced expansion or contraction without disrupting the document structure; and a compliance validation module (110) comprising a regulatory rule checker and an accessibility standards verifier configured to verify that the translated content adheres to data protection requirements, industry-specific regulations, and accessibility standards.

14. A method for document localization, comprising:
i. receiving an original document (202);
ii. pre-processing the original document (203);
iii. extracting text from the original document and applying contextual error correction using an AI-powered OCR engine (102) and natural language processing (NLP) models (204);
iv. translating the corrected text into a target language while preserving semantic intent, domain-specific terminology, and cultural nuances (205);
v. maintaining document formatting by preserving font styles, text alignment, spacing, and embedded graphical elements during translation (206); and
vi. dynamically adjusting the translated text layout to fit within the original document constraints, ensuring compatibility with different text expansion or contraction rates (206).
15. The method as claimed in claim 14, further comprising receiving real-time user feedback and adapting the translation and layout preservation based on the feedback.