A new table interpretation methodology with little knowledge base

Neves, Luiz Antônio Pereira; Carvalho, João; Facon, Jacques; Bortolozzi, Flávio

doi:10.1145/1141277.1141470

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…Character extraction problems have been solved by some researchers using variant methods. Neves et al (2006) proposed the cell extraction method for Table Form Segmentation which consists of steps such as initially locating and extracting the intersections of table lines. The weakness of this method is that the process involved complicated table extractions.…”

Section: Fig 1: Example Of Data Filled Boxesmentioning

confidence: 99%

“…The data filled box is a table with small squares designed to be filled with one character in each of the square spaces. A data filled box can be generally defined as a structured document composed by cells delimited by vertical line segments (Neves et al, 2006). Cells can be blank or filled with data, either printed or handwritten, as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Handwritten Characters Extraction from Form Based on Line Shape Characteristics

Al-Faris

Mohamad²,

Ngah

et al. 2011

Journal of Computer Science

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Problem statement: Data entry form is a convenient and successful tool for information collection by filling in the sheets using pen and handwriting. One of the most important fields in these forms is the data filled boxes. Extracting the handwriting from the data entry forms is important for many purposes such as in documenting and archiving. The extraction process is also important in situations such as when it is necessary to the handwritten recognition process. Approach: A simple and effective approach is presented to extract handwritten characters, including digits and letters of any language from data filled boxes of data entry form and to deal with cases of overlaps between the handwritten characters and boxes lines. The proposed approach is based on line shape characteristic by detecting and removing the vertical and horizontal straight boxes lines, while preserving the curved lines which represent the handwritten characters. The problem of the handwritten characters overlapping with the data filled boxes line is solved using morphology dilation to reconstruct the broken characters after the removal of the boxes lines. Results: Experimental results have demonstrated that the proposed approach can extract handwriting from data filled boxes with overall 94.052% for data collection of 150 forms. Conclusion: The proposed algorithm has been successfully implemented and tested to achieve the objectives of handwritten extraction of any language from data filled boxes. However, this work could not deal with situations whereby the characters touch other immediate characters

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Section: Fig 1: Example Of Data Filled Boxesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Handwritten Characters Extraction from Form Based on Line Shape Characteristics

Al-Faris

Mohamad²,

Ngah

et al. 2011

Journal of Computer Science

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“…Thereby, a threshold has been created for distinguishing if the value calculated for the compactness factor corresponds to that of an artefact or to a straight line segment of a table cell. For determining threshold value, compactness factors from more than 30 different artefacts were submitted to exploratory data analysis [19][22] [23], characterizing a homogenous distribution with a confidence level of 99%. The range of variation ± 2.576* , where and are the mean and standard deviation respectively, produces inferior and superior limits of 1.21688 and 1.37419, respectively.…”

Section: Artefact Identificationmentioning

confidence: 99%

A table-form extraction with artefact removal

Neves

Carvalho

Facon

et al. 2007

Proceedings of the 2007 ACM Symposium on Applied Computing

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Abstract:In this paper we present a novel methodology to recognize the layout structure of handwritten filled table-forms. Recognition methodology includes locating line intersections, correcting wrong intersections produced by what we call artefacts (overlapping data, broken segments and smudges), extracting correct table-form cells and using as little previous tableform knowledge as possible. To improve layout structure recognition, a novel artefact identification and deletion method is also proposed. To evaluate the effectiveness of the methodology, a database composed of 350 handwritten filled table-form images damaged by different types of artefacts was used. Experiments show that the artefact identification method improves performance of the table-forms structure extractor that reached a success rate of 85%.

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A new table interpretation methodology with little knowledge base

Cited by 2 publications

References 20 publications

Handwritten Characters Extraction from Form Based on Line Shape Characteristics

Handwritten Characters Extraction from Form Based on Line Shape Characteristics

A table-form extraction with artefact removal

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