Anatomy of a form reader

Lam, Stephen W.; Javanbakht, L.; Srihari, S.N.

doi:10.1109/icdar.1993.395685

Cited by 33 publications

(12 citation statements)

References 4 publications

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“…The main aim of form processing is to extract user filled-in data and associate it with the corresponding pre-printed data in order to store this tuple in a database for eventual retrieval purposes [1,5,6,8]. In a traditional form document processing system [1], there are two parallel paths.…”

Section: Motivationmentioning

confidence: 99%

An SGML based viewer for form documents

Atalay

Arslan

1999

Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318)

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This study proposes a viewer for visual reconstruction and retrieval of form documents. The structure of the form document and the filled-in data are stored in a Standard Generalised Markup Language (SGML) instance. Document Style Semantics and Specification Language (DSSSL) instance holds style properties of the data. Filled-in data is kept in a relational database. The viewer uses SGML and DSSSL instances as input and visually reconstructs a form document image by retrieving the corresponding filled-in data from database.

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Section: Motivationmentioning

confidence: 99%

An SGML based viewer for form documents

Atalay

Arslan

1999

Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318)

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“…The purpose of preprocessing is two-fold: First, we deskew the document so the parallel lines are oriented horizontally or vertically; second, we filter out text strokes to diminish their intervention on line detection. The skew of a document can be estimated using the text [26], or using the extracted line segments if lines are available on the document [27]. We proposed a coarse-to-fine skew estimation method, which is similar to [27].…”

Section: Model-based Form Processingmentioning

confidence: 99%

A parallel-line detection algorithm based on HMM decoding

Zheng

Doermann

2005

IEEE Trans. Pattern Anal. Machine Intell.

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The detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction from rule lined paper. These tasks can be very challenging in degraded documents where the lines are severely broken. In this paper, we propose a novel model-based method which incorporates high-level context to detect these lines. After preprocessing (such as skew correction and text filtering), we use trained Hidden Markov Models (HMM) to locate the optimal positions of all lines simultaneously on the horizontal or vertical projection profiles, based on the Viterbi decoding. The algorithm is trainable so it can be easily adapted to different application scenarios. The experiments conducted on known form processing and rule line detection show our method is robust, and achieves better results than other widely used line detection methods.

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“…Several attempts to develop form processing and reading methods have been reported [1][2][3][4][5][6][7]. These are roughly classified into two categories:…”

Section: Introductionmentioning

confidence: 99%

“…Methods which select particular model knowledge for an input form from a model database, and extract data from the input data by simple model fitting based on geometric feature matching [1][2][3][4]. 2.…”

Section: Introductionmentioning

confidence: 99%

Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

Ishitani

2000

Pattern Analysis & Applications

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A new method of image understanding for forms based on model matching is proposed in this paper as the basis of an OCR which can read a variety of forms. The outline of this method is described as follows. First, ruled lines are extracted from the input image of a form. After that, several lines are grouped as one to be recognised as data corresponding to a sub-form. These lines and subforms are both used for understanding the form, taking into account their feature attributes and the relationships between them. Each feature in the input image of a form is expected to correspond to a feature in one of the model forms, which are described as structured features. This correspondence is represented by a node in an association graph, where an arc represents compatible correspondences established on the basis of feature relationships. The best match is found as the largest maximal clique in the association graph. Experimental results show the method is robust and effective for document images of poor quality, and also for various styles of forms.

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Anatomy of a form reader

Cited by 33 publications

References 4 publications

An SGML based viewer for form documents

An SGML based viewer for form documents

A parallel-line detection algorithm based on HMM decoding

Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

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