Financial document processing based on staff line and description language

Tang, Yuan Yan; Suen, Ching Y.; Yan, Chang; Cheriet, Mohamed

doi:10.1109/21.376488

Cited by 31 publications

(24 citation statements)

References 8 publications

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“…In known form processing the number of lines and the gaps among these lines can be used as a priori knowledge and stored as references in form templates. These ideas have been presented in previous work to improve detection accuracy and reduce false alarms [14,1,15,16]. But the usage of a priori knowledge in the above applications is ad hoc and lacks a systematic representation.…”

Section: Rule Line Detectionmentioning

confidence: 99%

“…Line detection is widely used in form detection and interpretation [13,11,1], engineering graph interpretation [36], bank check/invoice processing [16,15], and optical music recognition (OMR) [37]. Among many algorithms proposed in the literature, the Hough transform method and its variations are widely used [38,9].…”

Section: Related Work On Line Detectionmentioning

confidence: 99%

“…Millions of form documents, such as tax return forms, health insurance forms, airline vouchers, checks, and bank slips, are processed everyday [15,16,61,62,63,19].…”

Section: Introductionmentioning

confidence: 99%

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Handwriting Identification, Matching, and Indexing in Noisy Document Images

Zheng¹

2006

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Throughout history, handwriting has been the primary means of recording information that is persevered across both time and space. With the coming of the electronic document era, we are challenged with making an enormous amount of handwritten documents available for electronic access. Though many handwritten documents contain only handwriting, now, more are mixed with printed text, noise, and background patterns. The mixture of handwriting with other components presents a great challenge for making an original document electronically accessible.Many handwritten documents come together with a special background pattern, rule lines, which are printed on the paper to guide writing. After digitization, rule lines will touch text and cause problems for further document image analysis if they are not detected and removed. In this dissertation, we present a rule line detection algorithm based on hidden Markov model (HMM) decoding, achieving both high detection accuracy and a low false alarm rate. After detection, line removal is performed by line width thresholding.Handwriting often mixes with printed text, such as signatures and annotations on a business letter. Handwriting in a printed document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content. The data set we are processing is noisy, which makes the problem more challenging. In this dissertation, we first segment the document at a suitable level, and then classify each segmented block as machine printed text, handwriting, or noise. Markov random field (MRF) based post-processing is exploited to refine the classification results.The identified handwriting may be further analyzed. In this dissertation, we propose a novel point-pattern based handwriting matching technique and apply it for handwriting synthesis and retrieval. We formulate point matching as an optimization problem trying to preserve the local neighborhood structures. After establishing the correspondence between two handwriting samples, we warp one sample toward the other using the thin plate spline (TPS) deformation model to synthesize new handwriting samples. We also apply our matching algorithm for handwriting retrieval since it is much easier to define robust features based on the matching

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Section: Rule Line Detectionmentioning

confidence: 99%

Section: Related Work On Line Detectionmentioning

confidence: 99%

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Handwriting Identification, Matching, and Indexing in Noisy Document Images

Zheng¹

2006

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“…To emphasize the pixels of the ruling lines, the authors used the binarization algorithm of Giuliano et al [19]. This algorithm uses 5 parameters K 1 ,…, K 5 . In our implementation, the authors' suggestion was followed and it was set K 5 =K 2 , while K 4 was discarded.…”

Section: State Of the Artmentioning

confidence: 99%

“…Many of the suggested algorithms concern specific type of document form [4][5], while others can fail in the presence of broken or skewed lines or in the case that text and ruling lines are extremely overlapped [6]. In the last case, a decision has to be made if a black pixel belongs to a line or a character, in order to turn it off or keep it on, respectively.…”

Section: Introductionmentioning

confidence: 99%

An image processing self-training system for ruling line removal algorithms

Prokopiou

Kavallieratou

Stamatatos

2013

2013 18th International Conference on Digital Signal Processing (DSP)

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Abstract-Ruling line removal is an important pre-processing step in document image processing. Several algorithms have been proposed for this task. However, it is important to be able to take full advantage of the existing algorithms by adapting them to the specific properties of a document image collection. In this paper, a system is presented, appropriate for fine-tuning the parameters of ruling line removal algorithms or appropriately adapt them to a specific document image collection, in order to improve the results. The application of our method to an existed line removal algorithms is presented.

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Robust matching with form model for faxed general form understanding

Hirano

Okada

Yoda

2004

Electron Comm Jpn Pt III

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SUMMARYFAXOCR is a device that reads handwritten characters on a faxed form. However, it is difficult to extract fields for character recognition when the form image includes deformation or noise. In order to cope with such a problem, special forms have been used with constraints on the format structure. This paper proposes a method of field extraction in which a general form can be read without constraints on the form structure. In the method, the feature points extracted from the form image and the feature points of the form model are matched. The deformation of the form image is estimated, and the position of the field is determined. Since the feature points can be extracted from the tables, character sequences, and illustrations which are contained in ordinary forms, the method can be applied to general forms with diversified structures. The estimation of deformation is robust to disappearance of feature points or to new appearance of excessive feature points, and the method is robust to degradation of the form image due to noise and other disturbances. The proposed method was applied to 650 general form images of 50 different kinds with deformations inherent to fax, and its effectiveness was demonstrated. The method has also been applied to field data, and performance at a practical level (form rejection rate of 3.5%) has been obtained

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Financial document processing based on staff line and description language

Cited by 31 publications

References 8 publications

Handwriting Identification, Matching, and Indexing in Noisy Document Images

Handwriting Identification, Matching, and Indexing in Noisy Document Images

An image processing self-training system for ruling line removal algorithms

Robust matching with form model for faxed general form understanding

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