System for intelligent extraction of document data using layout-oriented machine learning and post-processing rules

Abstract

A system (100) for intelligent document data extraction, the system comprising: a document input and capture module (1) configured to receive one or more source documents in the form of digital images, scanned images, in Portable Document Format, or as electronically generated documents; a preprocessing and normalization module (2) operatively connected to the document input and capture module (1) and configured to normalize the one or more source documents by performing at least one of the following operations: image enhancement, skew correction, alignment correction, noise reduction, contrast adjustment, character alignment, and text area refinement;a layout analysis and document structure recognition module (3) configured to identify and classify layout components of the one or more source documents, including text blocks, tables, key-value ranges, headers and footers, stamps, signatures, and reading order relationships; a layout-aware machine learning extraction module (4) configured to extract target data fields from the one or more source documents using a machine learning model trained on both textual and spatial layout features; a confidence scoring and validation module (5) configured to assign confidence values ​​to extracted data fields and detect ambiguous, inconsistent, incomplete, or unreliable results;a module (6) for post-processing rules and data correction, configured to refine the extracted data fields by applying one or more predefined and / or dynamically generated post-processing rules; and a module (7) for output generation, feedback learning, and integration, configured to generate structured output data and transfer the structured output data to one or more downstream applications, storage repositories, or application programming interfaces.

Description

INVENTION AREA The present invention relates generally to the field of intelligent document processing, data extraction automation, and machine learning-supported information retrieval. In particular, the invention relates to a system for extracting structured, semi-structured, and unstructured information from digital and scanned documents by combining layout-aware machine learning with rule-based post-processing, validation, correction, and output integration. BACKGROUND OF THE INVENTION The subject matter discussed in the background section should not be considered prior art solely because it is mentioned therein. Likewise, a problem mentioned in the background section or related to its subject matter should not be considered to be prior art. The subject matter in the background section merely presents various approaches, which could themselves also be inventions. Large amounts of business, legal, medical, financial, educational, logistical, and administrative information are stored in the form of invoices, forms, receipts, contracts, certificates, identification documents, statements, reports, and other documents. Such documents may exist in native digital formats or be captured as scanned images, photos, or uploaded files. Manually extracting relevant data from these documents is time-consuming, error-prone, inconsistent, and difficult to scale. Conventional optical character recognition (OCR) systems primarily focus on recognizing text characters and often fail to accurately capture document structure, contextual relationships, reading order, table boundaries, and key-value mappings. As a result, the extraction of meaningful fields such as names, dates, addresses, invoice numbers, totals, account details, clauses, or tabular entries can remain inaccurate even when the text is correctly recognized. These shortcomings become even more pronounced when documents have varying formats, complex layouts, multilingual content, or are affected by noise, skewness, low resolution, stamps, signatures, handwritten markings, or scan artifacts. Existing document extraction systems often rely heavily on either rigid templates or generic machine learning models. Template-based systems struggle to adapt to changing layouts and document formats, while purely statistical extraction systems can produce inaccurate results without reliable correction, reconciliation, or validation. Furthermore, traditional systems often lack effective post-processing mechanisms to enforce formatting rules, business logic, field consistency, or compliance requirements. They also frequently fail to maintain field-level traceability, correction history, and feedback loops for long-term improvement of extraction performance. Accordingly, there is a need for an advanced system capable of receiving documents from various sources, intelligently understanding layout and document structure, extracting target information through layout-aware machine learning, validating extracted content, applying post-processing rules for refinement, and generating structured results for downstream use. Such a system must also support continuous improvement through user feedback, traceability during review, and adaptable integration into enterprise workflows. The use of any examples or exemplary formulations (e.g., "as") in relation to certain embodiments described herein serves only to better illustrate the invention and does not constitute a limitation of the scope of the otherwise claimed invention. No formulation in the description should be interpreted as referring to an unclaimed element that is essential to carrying out the invention. The information disclosed above in this "Background" section is provided solely for the purpose of better understanding the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY Before describing the systems presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly presented in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application. The present invention provides a system (100) for the intelligent extraction of document data using layout-aware machine learning and post-processing rules. The system (100) comprises a document input and capture module (1), a preprocessing and normalization module (2), a module for layout analysis and understanding of the document structure (3), a module for layout-aware extraction using machine learning (4), a module for evaluating and validating confidence (5), a module for post-processing rules and data correction (6), and a module for output generation, feedback learning, and integration (7). During operation, one or more source documents are received from various input channels via the document input and capture module (1). The incoming documents are prepared and normalized by the preprocessing and normalization module (2) to improve readability and the quality of subsequent extraction. The layout analysis and document structure understanding module (3) then interprets the geometric and logical structure of the document by identifying areas such as headers and footers, tables, paragraphs, key-value blocks, signatures, and stamps, while simultaneously determining the reading order and positional relationships. The processed, layout-aware representation is provided to the layout-aware machine learning extraction module (4), which extracts target fields, semantic entities, relational data, metadata, and content relationships. The extracted results are evaluated by the confidence scoring and validation module (5), which calculates confidence scores field by field and identifies anomalies, missing values, inconsistencies, or uncertain results. The extracted data is then refined by the post-processing rules and data correction module (6), using one or more logic sets, including regular expression rules, checksum rules, business logic, domain dictionaries, field dependency rules, and contextual matching.Finally, the Output Generation, Feedback Learning and Integration module (7) formats the refined output into one or more structured formats and enables integration with external systems, while capturing user feedback and correction history for continuous learning and performance optimization. BRIEF DESCRIPTION OF THE DRAWING To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments thereof, which are illustrated in the accompanying drawing. It is understood that this drawing represents only illustrative embodiments of the invention and is therefore not to be regarded as limiting its scope. The invention is described and explained with additional specificity and detail using the accompanying drawing. To make the advantages of the present invention easily understandable, a detailed description of the invention is discussed below in conjunction with the accompanying drawing, although it should not be assumed that the scope of the invention is limited to the accompanying drawing in which: Fig. 1 shows a block diagram representation of the system (100) for intelligent document data extraction using layout-aware machine learning and post-processing rules. DETAILED DESCRIPTION The present invention relates to a system (100) for intelligent document data extraction using layout-aware machine learning and post-processing rules. Fig. 1 shows a detailed block diagram representation of the system (100) for intelligent document data extraction using layout-aware machine learning and post-processing rules. The present invention will now be described in detail with reference to exemplary embodiments. However, the following description serves only for illustration and should not be interpreted as limiting the scope of the invention. The present invention relates to a system (100) designed for the intelligent extraction of data from documents of various types, formats, and layouts. The system (100) is particularly suitable for extracting information from invoices, receipts, forms, certificates, contracts, utility bills, bank statements, identity documents, educational documents, health documents, insurance documents, customs or logistics documents, compliance documents, and other structured, semi-structured, or unstructured data sets. The document input and capture module (1) is configured to receive one or more source documents from one or more capture channels. In one embodiment, the source document can be obtained from scanner hardware, mobile camera images, multifunctional office devices, drag-and-drop upload portals, web applications, enterprise document archives, email attachments, cloud storage locations, workflow queues, or application interfaces. The document input and capture module (1) can also receive metadata associated with the document, such as a source identifier, a timestamp, sender information, a document category, a region, a user-selected extraction profile, or a document priority level. The received document is forwarded to the preprocessing and normalization module (2). The preprocessing and normalization module (2) is configured to prepare the document for downstream machine reading and extraction by performing one or more enhancement operations. Such operations may include distortion correction, noise reduction, orientation correction, shadow removal, edge cropping, contrast adjustment, background suppression, binarization, blur correction, page splitting, line sharpening, character alignment, normalization of multilingual fonts, and OCR-assisted text alignment. In some embodiments, the preprocessing and normalization module (2) can automatically detect the quality level of the incoming document and selectively apply an enhancement sequence based on the detected document type and image condition. After preprocessing, the document is analyzed by the Layout Analysis and Document Structure Understanding Module (3). This module is configured to understand the two-dimensional and logical structure of the source document. In one embodiment, the Layout Analysis and Document Structure Understanding Module (3) identifies text zones, headings, subheadings, paragraphs, tabular sections, line-column boundaries, labels, associated values, seals, checkboxes, handwriting areas, signatures, and page-level subdivisions. The module can generate a document layout map that includes boundary coordinates, structural hierarchies, proximity relationships, token grouping metadata, and reading order. Such document understanding enables the system to distinguish between similar text in different areas and to infer semantic meaning from relative positioning.The processed document representation is then provided to the layout-aware machine learning extraction module (4). The layout-aware machine learning extraction module (4) is configured to extract target fields from the document by considering both textual and layout information. In one embodiment, the module utilizes one or more machine learning models, including transformer-based models, graphical neural networks, vision-language models, attention-based networks, sequence-labeling models, or hybrid extraction architectures. These models can be trained to identify field labels, values, entity names, dates, numeric values, table items, addresses, identifiers, classifications, and relational pairs.In contrast to conventional pure OCR systems, the layout-aware machine learning extraction module (4) interprets the positional context, the “ ” spatial relationships, the grouping behavior, document areas and cross-sectional dependencies during extraction. In some embodiments, the layout-aware machine learning extraction module (4) can also perform a document type classification prior to extraction to select a relevant extraction pipeline. For example, an invoice extraction profile can prioritize invoice number, VAT identification number, supplier data, line items, tax values, and payment amounts; a contract extraction profile can identify contracting parties, dates, obligations, clauses, signatures, and renewal conditions; and an identity document extraction profile can recognize name, date of birth, identity number, and government information. The extraction module can therefore adapt its extraction behavior based on the content, layout pattern, and classification results. The extracted results are then forwarded to the Confidence Assessment and Validation module (5). This module is configured to generate confidence measures corresponding to the extracted results at the field and document levels. The Confidence Assessment and Validation module (5) can assess prediction probabilities, OCR reliability, layout mapping confidence, semantic consistency, field dependencies, rule matching reliability, and historical correction patterns. If a field does not meet a predefined threshold, the module can flag that field for review, secondary extraction, application of alternative rules, or manual validation. The confidence assessment and validation module (5) can also validate the extracted data against one or more criteria, including expected syntax patterns, numeric range checks, date validity checks, character class restrictions, the presence of mandatory fields, cross-field relationships, logical dependencies, duplicate detection, and source-type-specific validation logic. For example, if the invoice amount does not match the sum of the line items and taxes, or if an identification number fails format validation, the field can be marked as inconsistent and passed on for correction. The refined validation output is then processed by the post-processing rules and data correction module (6). This module is an essential feature of the invention and is configured to apply deterministic, semi-deterministic, and context-aware post-processing logic to improve extraction accuracy. In one embodiment, the post-processing rules and data correction module (6) applies regular expression rules to standardized patterns such as invoice numbers, tax IDs, account numbers, dates, postal codes, and telephone numbers. In another embodiment, the module applies business rules, such as the mandatory pairing of supplier name and GST number, arithmetic checks of tax totals, or consistency between form labels and field contents. The post-processing rules and data correction module (6) can further apply domain dictionaries, semantic normalization tables, abbreviation expansion rules, language normalization logic, context-aware replacement rules, fuzzy correction logic, template-specific mappings, or field dependency rules. For example, if the extraction model identifies a probable field value with low confidence, but that value substantially matches a known entry in the supplier database or an expected domain lexicon, the module can correct or normalize that field accordingly. In some embodiments, the module can dynamically select a rule set based on document type, source metadata, previous successful extraction patterns, region-specific configurations, language, or customer-specific workflows. In some embodiments, the post-processing rules and data correction module (6) can operate iteratively. If a field remains unresolved after an initial rule run, a secondary rule sequence can be called. If the inconsistency persists, the field can be passed to a queue for manual review, preserving the originally extracted value, the corrected candidate value, the confidence score, and the applied rule path for traceability. After post-processing and correction, the data is forwarded to the Output Generation, Feedback Learning, and Integration (7) module. This module is configured to generate structured output data in one or more machine-readable formats, including JSON, XML, CSV, relational database records, spreadsheet-compatible structures, API payloads, enterprise workflow messages, and audit logs. The Output Generation, Feedback Learning, and Integration (7) module can transfer the extracted and validated data to downstream systems such as ERP platforms, CRM systems, compliance engines, accounting software, claims management platforms, legal repositories, digital archives, search systems, and analytics dashboards. In one embodiment, the output generation, feedback learning, and integration module (7) further comprises a feedback capture engine configured to receive user corrections, approval results, rejected extraction instances, manually entered values, and revised mappings. Such feedback can be stored and used to retrain, optimize, or adapt the layout-aware machine learning extraction module (4) and / or to modify the rule logic used in the post-processing rules and data correction module (6). Thus, the system (100) is able to continuously improve its performance over time. The output generation, feedback learning, and integration module (7) can also include an audit and traceability engine configured to store document identifiers, extraction timestamps, rule execution history, field confidence logs, source metadata, model version information, corrective actions, user review status, and output delivery records. Such traceability is particularly useful in regulated environments where proof of extraction logic, review path, and change history is required. In an example workflow, a scanned invoice is received via the document input and capture module (1). The preprocessing and normalization module (2) improves the quality of the invoice image and aligns the content. The layout analysis and document structure understanding module (3) recognizes invoice header areas, seller and buyer blocks, the invoice number area, the item table, and the totals section. The layout-oriented machine learning extraction module (4) extracts fields such as invoice number, invoice date, supplier data, item descriptions, quantities, taxable values, tax amounts, and total amount. The confidence assessment and validation module (5) checks reliability at the field level and verifies mathematical consistency.The Post-Processing Rules and Data Correction module (6) applies format validation and logic for overall reconciliation, corrects detected inconsistencies where possible, and flags uncertain results as needed. The Output Generation, Feedback Learning, and Integration module (7) then exports the validated output to an accounting system and saves the extraction history for auditing purposes and future improvements. In another exemplary embodiment, the source document can be a contract. In such a case, the layout analysis and document structure understanding module (3) identifies clauses, parties, signature areas, dates, defined terms, appendices, and obligations. The layout-aware machine learning extraction module (4) then extracts relevant metadata at the clause level and entity relationships. The post-processing rules and data correction module (6) can apply legal wording normalization, date dependency checks, rules for obligation tags, and signature validation. The structured output can then be submitted to a contract lifecycle management system. Accordingly, the present invention offers a unified, intelligent, adaptable and scalable system for the extraction of document data, wherein the interaction of modules (1) to (7) significantly improves the extraction accuracy, consistency, reliability, verifiability and operational efficiency. ADVANTAGES OF THE INVENTION The present invention provides a layout-aware and context-sensitive extraction system that is capable of processing various document formats without being dependent on a single rigid template. The invention improves extraction accuracy by combining document enhancement, structural understanding, machine learning-based field extraction, trust-based validation, and rule-based correction in an integrated architecture. The invention enables automated post-processing and data normalization, thereby reducing manual intervention and improving the suitability of the extracted results for use in businesses. The invention supports dynamic learning from user feedback and historical corrections, thereby continuously improving system performance over time. The invention provides traceable, verifiable and integrable results, making it suitable for regulated, internal and document-intensive processing environments. The foregoing description serves to illustrate preferred embodiments of the invention and is not intended to limit the scope of the invention. Variations and modifications such as alternative deployment topologies, additional runtime enforcement points, alternative telemetry sources, and alternative types of prediction models can be implemented without departing from the spirit and scope of the present invention as defined by the claims.

Claims

A system (100) for intelligent document data extraction, comprising: a document input and capture module (1) configured to receive one or more source documents in the form of digital images, scanned images, in Portable Document Format, or as electronically generated documents; a preprocessing and normalization module (2) operatively connected to the document input and capture module (1) and configured to normalize the one or more source documents by performing at least one of the following operations: image enhancement, skew correction, alignment correction, noise reduction, contrast adjustment, character alignment, and text area refinement;a layout analysis and document structure recognition module (3) configured to identify and classify layout components of the one or more source documents, including text blocks, tables, key-value ranges, headers and footers, stamps, signatures, and reading order relationships; a layout-aware machine learning extraction module (4) configured to extract target data fields from the one or more source documents using a machine learning model trained on both textual and spatial layout features; a confidence scoring and validation module (5) configured to assign confidence values ​​to extracted data fields and detect ambiguous, inconsistent, incomplete, or unreliable results;a module (6) for post-processing rules and data correction, configured to refine the extracted data fields by applying one or more predefined and / or dynamically generated post-processing rules; and a module (7) for output generation, feedback learning, and integration, configured to generate structured output data and transfer the structured output data to one or more downstream applications, storage repositories, or application programming interfaces. System (100) according to claim 1, wherein the document input and capture module (1) is further configured to receive documents from multiple sources, including scanner devices, mobile capture devices, email attachments, cloud storage, enterprise document management systems and web-based upload interfaces. System (100) according to claim 1, wherein the preprocessing and normalization module (2) is configured to perform adaptive document enhancement based on the detected document type, including automatic border removal, background suppression, blur reduction, resolution normalization, font recognition, and multilingual OCR alignment. System (100) according to claim 1, wherein the module (3) for layout analysis and understanding of the document structure is configured to generate a document layout map comprising position coordinates, hierarchical areas, neighborhood relationships, cell boundaries, paragraph groupings and metadata relating to the reading order, for use by the layout-aware module (4) for extraction by machine learning. System (100) according to claim 1, wherein the layout-aware module (4) for machine learning extraction comprises one or more transformer-based, “ ”-graph-based, vision-language-based or hybrid neural network models configured to extract named entities, field labels, field values, table contents, relational pairs and semantic associations from the one or more source documents. System (100) according to claim 1, wherein the confidence assessment and validation module (5) is configured to compare extracted data fields against one or more validation criteria, including value range constraints, expected format constraints, cross-field dependency constraints, document-specific logic and duplicate detection criteria, and marks one or more extracted data fields for manual review if a confidence threshold is not reached. System (100) according to claim 1, wherein the module (6) for post-processing rules and data correction is configured to apply one or more of the following rules: regular expression rules, keyword rules, ontology-based rules, template-specific rules, business rules, checksum rules, cross-field matching rules, and context-driven correction rules to modify, correct, normalize, or validate the extracted data fields. System (100) according to claim 1, wherein the module (6) for post-processing rules and data correction is further configured to perform document-type-specific intelligent correction by selecting a rule set based on classification results, recognized layout patterns, source metadata or previously learned extraction behavior in connection with similar documents. System (100) according to claim 1, wherein the output generation, feedback learning and integration module (7) is configured to generate outputs in one or more machine-readable formats, including JSON, XML, CSV, spreadsheet-compatible output, database-ready records or workflow messages, and is further configured to receive corrected user feedback for retraining or fine-tuning the layout-aware machine learning extraction module (4) by means of “ " and / or for updating the post-processing rules in the post-processing and data correction module (6). System (100) according to claim 1, wherein the module (7) for output generation, feedback learning and integration further comprises an audit and traceability engine configured to store extraction history, field-level confidence logs, rule application history, user correction history, document version metadata and integration transaction records for compliance, review and continuous performance improvement.

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