Piece-wise painting technique for line segmentation of unconstrained handwritten text: a specific study with Persian text documents

Alaei, Alireza; Nagabhushan, P.; Pal, Umapada

doi:10.1007/s10044-011-0226-x

Cited by 25 publications

(9 citation statements)

References 15 publications

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“…Two algorithms were utilized for the task of text-line segmentation. The Potential Piece-wise Separation Line (PPSL) method [3] obtained an 89.43% segmentation accuracy, while the other method proposed by Alaei et al [4], outperformed PPSL with an accuracy of 94%. In both methods, each document was split vertically into stripes.…”

Section: Persian Handwritten Text Dataset (Phtd)mentioning

confidence: 99%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

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This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 56 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or semantic analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

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Section: Persian Handwritten Text Dataset (Phtd)mentioning

confidence: 99%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the implementation of the segmentation method Alaei et al (2011) used in this work, s is the only parameter to be considered. Since the width of the stripes should be small enough to provide appropriate segmentation, some values with respect to the image width (2.5% and 5% of image width) were considered to analyse the impact of the parameter (s) on the results.…”

Section: Segmentation and Patch Selection Parametersmentioning

confidence: 99%

“…Alaei et al (2011) is employed to approximately detect foreground information from document images for estimating their quality. The Piece-wise Painting Algorithm (PPA) is a powerful pre-processing technique evaluated on two dierent tasks, such as unconstrained handwritten text line segmentation Alaei et al (2011) and logo detection Alaei & Delalandre (2014).…”

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confidence: 99%

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Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

Alaei

Conte

Martineau

et al. 2018

Expert Systems with Applications

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The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-today life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is rst sampled into a set of patches. The extracted patches are then ltered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is nally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the eectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.

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“…The concept of the Hough transform is employed in the field of document analysis for many purposes such as skew and slant detection and text line segmentation (Likforman-Sulem et al, 1995;Louloudis et al, 2008;Alaei et al, 2011). The Hough transform is a popular technique for finding straight elements in images.…”

Section: R Ptak Et Almentioning

confidence: 99%

Projection–Based Text Line Segmentation with a Variable Threshold

Ptak

Zygadło

Unold

2017

International Journal of Applied Mathematics and Computer Science

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Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.

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Piece-wise painting technique for line segmentation of unconstrained handwritten text: a specific study with Persian text documents

Cited by 25 publications

References 15 publications

A Survey of Historical Document Image Datasets

A Survey of Historical Document Image Datasets

Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

Projection–Based Text Line Segmentation with a Variable Threshold

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