Evaluation of SVM, MLP and GMM Classifiers for Layout Analysis of Historical Documents

Wei, Hao; Baechler, Micheal; Slimane, Fouad; Ingold, Rolf

doi:10.1109/icdar.2013.247

Cited by 38 publications

(26 citation statements)

References 14 publications

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“…The 1.524 images from the second dataset have been created using the 127 original images and transformed using our 3D distortion model. The tests presented in [39,59] confirm the conclusion of [60] about the impact of the degradation level on re-training, either for a task of character recognition or layout extraction.…”

Section: Document Image Generation For Retraining Tasksupporting

confidence: 81%

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

et al. 2017

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Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth. DocCreator has been used in various experiments, showing the interest of using such synthetic images to enrich the training stage of DIAR tools.

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Section: Document Image Generation For Retraining Tasksupporting

confidence: 81%

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

et al. 2017

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“…With DocCreator any DIAR researcher can create complete groundtruthed images and increase the size of its document image database. After 5 years of several collaborations and test campaigns, DocCreator (or databases created with DocCreator) have been tested by different researchers and used to publish [25], [26], [27], [28] proving its utility for performance evaluation or retraining tasks. DocCreator is on an open source ongoing project.…”

Section: Discussionmentioning

confidence: 99%

Massive, Free and Reproducible Grountruthed Document Image Databases Generation with DocCreator

Journet¹,

Mansencal

Visani

2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Whether your research is focused on image restoration, layout analysis, text-graphic separation, binarization, OCR, etc. you need a groundtruthed database to train your method or to evaluate it. This article presents DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled groundtruth. With DocCreator, you can create complete synthetic images choosing the text, font, background and layout to use, add various realistic degradations (bleedthrough, light defect, paper deformation, ink degradation, etc.) on original images, or combine both to increase the size of your database. DocCreator comes as an online (easy to test version) and a desktop solution (fast calculation process, and no need to upload copyrighted data). DocCreator is useful for retraining tasks and to know precisely whether your algorithm is robust. It has already been used favorably and could help other DIAR researchers to produce and share groundtruthed databases.

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“…This process is based on the machine learning where feature extraction process is accomplished and a real-valued feature vector is formulated using pixel window. Later color and coordinate features [29] are applied with the help of pixel coordinates and values. Moreover, LBP (Local Binary Pattern) and Gabor feature extraction also implemented to formulate a robust feature vector.…”

Section: Literature Surveymentioning

confidence: 99%

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

2019

ijrte

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Segmentation is always an important step in designing an Optical Character Recognition (OCR) of any script. In this paper, we focus on the line and word segmentation in typewritten Gurmukhi script documents. In order to perform this task, we consider OCR based methodology where several processing steps are implemented. The typewritten documents suffer from several issues such as noise, skew, and quality of the document. In this work, we present a combined pre-processing scheme where document thresholding and skew detection and correction schemes are implemented where image thresholding is obtained using Niblack’s method and skew correction is carried out using gradient histogram algorithm and uniform orientation is obtained. Later, line segmentation scheme is applied where probability density function is applied to generate the text distribution in the probability map. Here, identifying the relation of the text to the exact line is a challenging task hence, we present a 2D-Gaussian modelling which helps to identify the text boundaries in the x and y direction. The proposed methodology is applied for typewritten Gurmukhi documents and an experimental study is carried out to show that the proposed approach achieves better performance when compared with the existing techniques

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Evaluation of SVM, MLP and GMM Classifiers for Layout Analysis of Historical Documents

Cited by 38 publications

References 14 publications

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

Massive, Free and Reproducible Grountruthed Document Image Databases Generation with DocCreator

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

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