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Publication PDF layout analysis and recognition method based on mixing of multiple neural networks

A technology of layout analysis and identification method, applied in the field of PDF layout analysis, can solve the problems of inability to provide physical and logical structure analysis, inability to distinguish text line headers and other structures, to improve recognition accuracy, alleviate model overfitting, and reduce impact Effect

Inactive Publication Date: 2020-03-06
重庆华龙网海数科技有限公司
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Problems solved by technology

[0005] In view of this, the object of the present invention is to provide a published PDF layout analysis and recognition method based on a mixture of multiple neural networks, which solves the problem of being unable to provide physical and logical structure analysis for the layout of published PDF files, that is, unable to distinguish text lines, titles, etc. structural problems

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  • Publication PDF layout analysis and recognition method based on mixing of multiple neural networks
  • Publication PDF layout analysis and recognition method based on mixing of multiple neural networks

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Embodiment Construction

[0033] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0034] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a publication PDF layout analysis and recognition method based on mixing of multiple neural networks, belongs to the technical field of image recognition and PDF layout analysis. The method comprises: using a multi-task training mode to recognize, segmenting and marking PDF layouts including paragraphs, titles and illustrations, locating text lines and then recognizing texts. According to the method, on the aspect of layout recognition, through a multi-task training mode, row and structure recognition annotation is completed at the same time, manual participation is not needed in the whole process, and PDF text structure information is effectively reserved. According to data with PDF text structure information obtained through layout analysis, a common Chinese dictionary for version data is constructed, and a text recognition model is trained in a targeted manner, so that the recognition precision of the model in a PDF printed text recognition task is greatly improved. The recognized text also has structural information, an original PDF layout structure is restored, and subsequent secondary editing, electronic book manufacturing and book content knowledge mining are also facilitated.

Description

technical field [0001] The invention belongs to the technical field of PDF layout analysis, and relates to a publishing PDF layout analysis and recognition method based on a mixture of multiple neural networks. Background technique [0002] With the rise of big data and artificial intelligence technology, many traditional industries have brought opportunities for digital and intelligent transformation, including the publishing industry. [0003] One of the biggest challenges in the transformation of the publishing industry is that it has a large amount of unstructured data resources that are difficult to process, such as: books, papers, etc. In order to meet the needs of publishing and printing, such resources are mostly contained in PDF format files. Due to the complexity of the PDF format itself, the diversity of publishing and printing requirements, and the lack of PDF editing specifications, the existing PDF books and paper data cannot be edited again, and the text info...

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Application Information

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IPC IPC(8): G06F40/205G06F40/279G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/045
Inventor 李翀卢云龙蒋路曦
Owner 重庆华龙网海数科技有限公司
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