11 results about "Document layout" patented technology

A system for semantic parsing of an input digital document using visual and textual features is provided. The system includes a user interface, a document layout classification module, a semantic recovery module, and a text structuring module. The user interface enables users to upload the input digital document. The document layout classification module processes the document to categorize its elements based on page images and textual data, outputting layout information with tags and locations. The semantic recovery module uses this layout information to derive content, including tables, lists, and charts, and generates a hierarchical structure. The text structuring module organizes tokens based on the layout tags, groups text into sections by topic relevance, and handles page boundaries, producing another hierarchical structure.

The application relates to the technical field of knowledge question answering, in particular to a knowledge question answering method based on a multi-modal document understanding large model of a graph structure, which comprises the following steps: obtaining a target task and a target document graph corresponding to the target task; generating a target heterogeneous document graph corresponding to the target document graph according to the target document graph; inputting the target task and the target heterogeneous document graph into a preset large model to obtain a target question and answer text corresponding to the target task; the method can achieve higher accuracy and robustness on multiple public benchmark tasks (such as document question answering, graph table question answering and table understanding); the method has excellent generalization ability for novel and complex document layouts; and the method realizes more simple and efficient system deployment and application through an end-to-end unified architecture.

The application relates to a low-cost entity labeling method and system based on user behavior analysis, which comprises the following steps: S1, data collection: using a state machine to provide a document layout service, collecting user historical documents, entity recognition results and user revision records; S2, data labeling: generating a labeling data set according to the entity recognition results, finding out suspicious incorrect labeling by using the user revision records and reconfirming to optimize the labeling data set; S3, model updating: training an NER model by using the labeling data set, and replacing the state machine in the step S1 with the NER model when the accuracy of the NER model exceeds that of the state machine. The method and system can obtain a higher labeling accuracy under the premise of less labeling workload.

The application provides a kind of based on agent's bidding document processing method, system and equipment, the method includes: the semantic analysis of bidding document processing task obtains task description information;Task graph is dynamically constructed based on task description information and preset skill dependency library, and layout constraint condition is bound to document layout subtask node.From the preset global tool registry, match candidate execution scheme, and determine the target execution scheme corresponding to the subtask node from each candidate execution scheme based on the multi-dimensional evaluation model;Checkpoint mark is added before document layout subtask node;Through preset execution state machine, the target execution scheme corresponding to each subtask node is driven in turn, and the asynchronous callback event of underlying document processing engine is monitored in the process of executing each subtask node, to obtain the underlying operation status code in real time;When monitoring based on underlying operation status code detects an exception, trigger the corresponding exception recovery strategy according to the exception type.

This application discloses a document segmentation method, apparatus, computer device, and storage medium. In response to a segmentation command, a document to be segmented is acquired; the document is parsed to obtain multiple text units; visual features related to the document layout are determined based on the text units; a segmentation score is calculated based on the visual features; and segmentation is performed based on the segmentation score. In this application, the visual layout information of the document is referenced from a visual layout perspective, avoiding the text extraction quality defects of treating the document as a plain text stream without relying on OCR. Instead, segmentation is performed by combining the visual geometric layout characteristics of the document when the user browses the text, conforming to the browsing patterns of users reading documents, accurately identifying document structural boundaries, and improving the accuracy of text segmentation.

A system for semantic parsing of an input digital document using visual and textual features is provided. The system includes a user interface, a document layout classification module, a semantic recovery module, and a text structuring module. The user interface enables users to upload the input digital document. The document layout classification module processes the document to categorize its elements based on page images and textual data, outputting layout information with tags and locations. The semantic recovery module uses this layout information to derive content, including tables, lists, and charts, and generates a hierarchical structure. The text structuring module organizes tokens based on the layout tags, groups text into sections by topic relevance, and handles page boundaries, producing another hierarchical structure.

This invention discloses a method and system for intelligent segmentation and instance division of multi-type regions in document layout, belonging to the field of computer vision technology. The invention employs a "quality-driven adaptive process scheduling" mechanism, dynamically allocating computing power and selecting processing branches based on document health; designs a "four-modal cross-attention fusion network" to achieve macro-meta-micro three-level refined region segmentation; proposes a "segmentation-repair bidirectional iterative coupling" model to solve the problem of insufficient segmentation accuracy for low-quality documents; adopts a "segmentation-desensitization integrated network" to eliminate the risk of data leakage during data transfer; constructs an "anchor-free multi-objective parallel segmentation network" to support batch processing of multiple mixed documents; and establishes a "region-level D-S evidence theory verification system" to ensure the authenticity and legality of the segmentation results.

The application discloses a kind of invisible anti-fake seal layout method based on character shape contour fine tuning, belong to seal anti-fake and font processing technical field.The method includes: obtaining the contour data of standard character shape;Identify the contour node area (total six types) of non-intersection stroke contact connection in character shape;Increase or move operation is executed to the contour node of selected area, to introduce slight deformation under the premise of maintaining the overall visual identification of character shape;The modified contour data is saved as anti-fake character shape contour, and the corresponding anti-fake font library is generated;Using the anti-fake font library in seal layout software carries out seal print design, and generates the seal pattern with invisible anti-fake feature.The application realizes the invisible anti-fake of seal print by local fine tuning of character shape contour, under the premise of not affecting normal use and identification of seal, is suitable for seal production, anti-fake document layout and other fields.

This invention relates to the field of database construction technology, and discloses a knowledge base construction method and system based on AI text parsing and hybrid retrieval. By integrating semantic relevance and document layout features through adaptive block segmentation, it achieves accurate identification of core semantic units in the text and maintains logical integrity in the division, fundamentally avoiding information fragmentation and laying the foundation for high-quality knowledge organization. Secondly, through a hybrid retrieval mechanism combining keyword, semantic vector retrieval, and multi-dimensional re-ranking, it effectively balances retrieval response speed with the depth and accuracy of results, meeting users' multi-level query needs from rapid location to in-depth correlation mining. Finally, through dynamic updates and automatic association mapping functions, it achieves real-time synchronization and intelligent association of newly added knowledge, not only ensuring the timeliness of the knowledge system but also proactively building a cross-document knowledge network, thereby breaking down information silos and enhancing the overall utilization value and discovery capability of knowledge.

This invention discloses a document parsing method, system, and readable storage medium based on a large-scale model collaboration, relating to the fields of intelligent document processing and artificial intelligence. It includes: acquiring a page image; inputting the page image into a first model to detect and locate the regions and categories of page elements, outputting location information and category labels; sorting the page elements according to the location information and category labels to generate a page element sequence; inputting image slices of the sorted page elements into a second model for content recognition based on categories, outputting structured recognition results; and generating structured data containing document structure and content based on the structured recognition results. This invention separates and collaboratively processes the document layout perception task and the content semantic understanding task, leveraging the respective advantages of large and small models to achieve automated parsing, reading order recovery, content recognition, and directory structure reconstruction of complex PDF documents, thereby outputting high-quality structured document data.

The application discloses a document layout recognition method, comprising: extracting text box information and hierarchical relationship of original document data to obtain training data; constructing an Albert model containing an Embedding layer as an initial recognition model; wherein the Embedding layer is constructed by text features, layout features, image features and page number features; training the initial recognition model by using the training data to obtain a recognition model; and recognizing a to-be-recognized document by using the recognition model to obtain the area category and hierarchical relationship of each text box in the to-be-recognized document. The accuracy and precision of automatic analysis of the document layout are improved. The application also discloses a document layout recognition device, a terminal device and a computer readable storage medium, which have the above beneficial effects.