Table form document analysis based on the document structure grammar

Amano, Atsushi; Asada, Naoki; Aoyama, Masahito

doi:10.1007/s10032-005-0008-3

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…For example, in absence of spatial feature, a table structure without border is highly difficult in understanding its context and structure. On the other hand by using spatial feature, understanding could be possible via boundary detection algorithms [11].…”

Section: Feature Enrichmentmentioning

confidence: 99%

DAG Based Feature Additive XML Schema Generation for Unstructured Text

Rajbabu

Selvaraj

2013

2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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Recent works on handling unstructured text employ multilevel filtering techniques for identifying the key terms in documents and then apply mining techniques to extract necessary information. Though these techniques are more efficient in information retrieval, they cannot be applied directly for information extraction, for documents that are more critical in context and also accuracy cannot be expected. Further, loss of hidden and significant information cannot be tolerated in data critical applications emerging based on unstructured documents. Hence, a novel idea of re-organizing the unstructured textual model into feature enriched structured graphical model by adding spatial, logical, lexical, syntactical and semantic features is proposed. The generated graph depicts relationships across the document at all levels from its micro level token to macro level document. Moreover, a structural pattern identification algorithm for generating an XML schema from the generated graph is also recommended. The experimental outcome for a real-time dataset is presented.

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Section: Feature Enrichmentmentioning

confidence: 99%

DAG Based Feature Additive XML Schema Generation for Unstructured Text

Rajbabu

Selvaraj

2013

2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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“…This system distills letters and the table structure from scanned graphics. Amano et al proposed a table form document analysis and synthesis system [24], [25]. This proposed system analyzes the table structure based on the string grammar, which is called the document structure grammar, and generates the synthesized documents.…”

Section: Introductionmentioning

confidence: 99%

“…[26], [27], they presented a structure analysis based on a graph grammar. In addition, they proposed TFML [25], [27] based on XML, which contains structure and layout information. The basic structure of TFML reflects the indication pattern of the document.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of XML Files and Their Database Registration from Tabular Form Specifications

Shiono

Arita

Miyadera

et al. 2012

Journal of Information Processing

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Various forms of tables have been used as tools for visualizing and arranging information in many fields. In addition, XML is widely used as a language for exchanging data. We have studied how documents are formally processed with software development tools. In this paper, we propose a system to create and manage tabular specifications based on an attribute graph grammar. A tabular form specification is represented by a marked graph, and its syntax is defined by an attribute NCE graph grammar. We add a new attribute that contains XML source codes of the tabular form specifications. The XML source codes are generated by evaluating the attribute and are automatically registered to the database. The specifications are then retrieved from the database. Our system can perform a characteristic retrieval for software specifications. The results may lead to a considerable improvement in the efficiency of human labor due to the use of a unified formal methodology based on graph theory and advanced retrieval.

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“…This example is taken from Wang [26], and uses terminology taken from the Chicago Manual of Style [27] to 'program' the system (e.g. as an attributed grammar with rules containing associated actions [22,23,24]). Given a model specification and an input file, the sequence of decisions to make is determined algorithmically (e.g.…”

Section: Recognizing Tables: a Decision-making Problemmentioning

confidence: 99%

Decision-Based Specification and Comparison of Table Recognition Algorithms

Zanibbi

Blostein

Cordy

2008

Machine Learning in Document Analysis and Recognition

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Summary. Recognition algorithms are difficult to write and difficult to maintain. There is need for better tools to support the creation, debugging, optimization, and comparison of recognition algorithms. We propose an approach that centers on a process-oriented description. The approach is implemented using a new scripting language called RSL (Recognition Strategy Language), which captures the recognition decisions an algorithm makes as it executes. This semi-formal process-oriented description provides a powerful basis for developing and comparing recognition algorithms. Based on this description, we describe new metrics related to the sequence of decisions an algorithm makes during recognition. The capture of intermediate decision outputs and these new process-oriented metrics greatly extend the limited information available from final results and traditional results-oriented metrics such as recall and precision. Using a simple example, we illustrate how these new metrics can be used to understand and improve decisions within a recognition strategy. We believe these new metrics may also be applied in machine learning algorithms that construct optimal decision sequences from sets of decisions and/or strategies.

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Table form document analysis based on the document structure grammar

Cited by 9 publications

References 15 publications

DAG Based Feature Additive XML Schema Generation for Unstructured Text

DAG Based Feature Additive XML Schema Generation for Unstructured Text

Automatic Generation of XML Files and Their Database Registration from Tabular Form Specifications

Decision-Based Specification and Comparison of Table Recognition Algorithms

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