Layout analysis method and system for automatic classification of test paper content

Abstract

The present invention proposes a layout analysis method and system for automatic classification of test paper content. The method includes: obtaining an input document image; extracting connected components of the document image to form an original set of connected components; The connected components are classified into text and non-text, and the first set of text connected components and the set of non-text connected components are obtained; for each connected component in the set of non-text connected components, the text components are detected and segmented, and the connected components of the non-text classification are obtained. The text components in the connected components, and add this component to the first text connected component set to obtain the second text connected component set; for each connected component in the second text connected component set, carry out the classification of printed characters and handwritten characters ; Output the classification result of the document image content. By adopting the method of the invention, the classification problem of elements is transformed into a global optimization problem for solving the maximum joint probability of all elements, so that the overall classification accuracy rate can be improved.

Description

technical field [0001] The invention relates to the technical field of electronic equipment, in particular to a layout analysis method and system for automatic classification of test paper contents. Background technique [0002] The layout analysis algorithm of complex document images occupies a vital position in the field of document analysis and recognition, especially with the application of deep learning in the field of text recognition in recent years, single character recognition, word recognition and string recognition have achieved great success. The very high accuracy rate makes layout analysis the bottleneck in the entire document analysis and recognition process. In many cases, there may be more than one type of content in a document, but multiple types of content such as text, geometric figures, illustrations, tables, formulas, and background noise. For the text category, there may be a mixture of printed text, handwritten text, different languages, different la...

AI Technical Summary

Problems solved by technology

However, the documents in this work are online handwritten documents, and the graph model structure used by the author is a linear chain conditional random field, which is not suitable for offline documents

LSTM-based classification [4] uses LSTM to model the context information of time series, but it tends to ignore the spatial context information, which may be crucial for classification

[0004] In general, although researchers have proposed many layout analysis methods for document content classification, they mainly focus on relatively simple document images.

For complex test paper document images, due to the rich and varied content contained in it, coupled with the complex and changeable layout structure, it brings great challenges to the existing layout analysis methods.

Although there are some methods based on structured prediction, the potential energy function or network structure used is still relatively elementary, and the structured prediction method based on the general undirected cyclic graph structure has not been fully studied, so it is still Does not cope well with document layout analysis tasks with complex content

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