Quantitative assessment of capabilities of colour models for pen ink discrimination in handwritten documents

Dansena, Prabhat; Pal, Rajarshi; Bag, Soumen

doi:10.1049/iet-ipr.2018.6616

Cited by 8 publications

(4 citation statements)

References 29 publications

(111 reference statements)

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“…Dasena et al [ 411 ] proposed a method to differentiate pen ink colors based on normalized color histogram distance. Seven blue and seven black pen ink samples on paper cheque-stock were assessed.…”

Section: Forensic Document Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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Section: Forensic Document Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…Further, MLP classifier is trained on the extracted feature set to identify whether pair of words are written using same pen or not. In [20], a quantitative assessment is presented to identify a suitable color model for detecting addition of new words. Further, a MLP classifier is trained on extracted statistical features for validating the selection of Y C b C r color model.…”

Section: Volume X Yyyymentioning

confidence: 99%

“…In order to separate foreground ink pixels from background, k-means clustering (k=2) has been performed in the gray scale version of the images. k-means clustering of intensities of the pixels in an image is also used for separating foreground and background pixels in several of the earlier works [18], [20]. Though, any good binarization technique can be adopted here.…”

Section: B Pre-processing Of Character Imagesmentioning

confidence: 99%

Generation of Synthetic Data for Handwritten Word Alteration Detection

2021

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Fraudsters often alter handwritten contents in a document in order to achieve illicit purposes. At times, this may result in financial and mental loss to an individual or an organization. Hence, ink analysis is necessary to identify such an alteration. Convolution Neural Network (CNN) can be used to identify such cases of alteration, as CNN has emerged as a monumental success in the field of computer vision for varieties of classification tasks. But, CNN requires large amount of labeled data for training. Hence, there is a need to generate a large dataset for the experiments relating to handwritten word alteration detection. Collection, digitization, and cropping of a large number of altered and unaltered handwritten words are tedious and time consuming. To overcome such an issue, an approach for synthetic word data generation is presented in this paper for handwritten word alteration detection experiments. This scheme is designed in such a way that the synthetically generated words are very similar to the original ones. In order to achieve this, handwritten character data set is prepared using 10 blue and 10 black pens. These handwritten characters are used for creating synthetic word alteration data set. The presented approach uses relatively less number of handwritten character images to create a huge word alteration data set. Further, deep learning models are trained on the synthetically generated data set for word alteration detection.

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“…Despite the active development of the DCA method and its wide application for chemical, 30 medical, 25,31,32 food, 33,34 and forensic 35,36 analysis, there are only pilot studies on the use of DCA for the analysis of the coloring components of pen inks. 9,37 However, it is the color change that is the result of long-term storage of the document and that can serve as an analytical signal for the rapid determination of the document age. Recently it has been shown that the artificial application of some external factors, e.g.…”

Section: Introductionmentioning

confidence: 99%