Layout analysis method and system for automatically classifying test paper contents

Abstract

The invention provides a layout analysis method and system for automatically classifying test paper contents. The method comprises the following steps: acquiring an input document image; Extracting communicating parts of the document image to form an original communicating part set; Performing text and non-text classification on each communication component according to the communication components of the document image, and obtaining a first text communication component set and a non-text communication component set; Detecting and segmenting the character components of each communication component in the non-text communication component set to obtain the character components adhered to the communication components of the non-text classification, and adding the character components into the first text communication component set to obtain a second text communication component set; Classifying the printed characters and the handwritten characters for each communication component in thesecond text communication component set; And outputting a classification result of the document image content. By the adoption of the method, the classification problem of the elements is converted into a global optimization problem for solving the maximum joint probability of all the elements, and therefore the overall classification accuracy 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...

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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.

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