Grayification: A meaningful grayscale conversion to improve handwritten historical documents analysis

Bouillon, Manuel; Ingold, Rolf; Liwicki, Marcus

doi:10.1016/j.patrec.2018.05.021

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…While the research into various aspects of automated systems for various tasks related to handwriting and signature analysis is significant, one relevant textbook [ 190 ] and104 articles [ [191] , [192] , [193] , [194] , [195] , [196] , [197] , [198] , [199] , [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] , [208] , [209] , [210] , [211] , [212] , [213] , [214] , [215] , [216] , [217] , [218] , [219] , [220] , [221] , [222] , [223] , [224] , [225] , [226] , [227] , [228] , [229] , [230] , [231] , [232] , [233] , [234] , [235] , [236] , [237] , [238] , [239] , [240] , [241] , [242] , [243] , [244] , [245] , [246] , [247] , [248] , [249] , [250] , [251] , [252] , ...…”

Section: Forensic Handwriting Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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Section: Forensic Handwriting Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…These methods generate grayscale images that are optimized for color and contrast rendering. However, when utilizing such optimization methodologies, the brightness ordering of the pixels is lost, which is undesirable in the preprocessing step of document analysis systems [30]. Figure 4 depicts the original and standard image of the grayscale.…”

Section: Grayscale Conversionmentioning

confidence: 99%

Analysis and development of brain tumor prediction model using deep neural network

Chhabra,

Bansal

2023

ITM Web Conf.

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The human brain consists of billions of living organisms and is very difficult to decipher because of its complexity. Brain tumors can be deadly, significantly impacting the quality of life and changing everything for patients and their loved ones. In today’s world, brain tumors are a leading cause of death in both children and adults. A high death percentage is caused due to the invasive properties of tumors. But it is inspiring that the survival rate might increase if the diagnosis is performed at the early stage [9]. Accurate detection of the brain tumor at an early stage can prolong the chance of survival of an infected patient [4]. Magnetic Resonance Imaging (MRI) is the most popular imaging technique used today for detecting brain tumors. Deep Neural Network techniques plays an important role in detecting brain tumors. This manuscript offers a brief analysis of studies conducted by various authors in the field of BT categorization and diagnosis from MRI images using Deep Neural Network (DNN). This paper also suggests a method for classifying and identifying brain tumors based on MRI pictures and supporting text using DNN and DWT.

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“…Compared to OCR, HTR features the challenge related to the high variability of characters in shape and size. A common strategy is performing specific data augmentation and preprocessing [4,9,23,28,37,38,50,52], while few works have faced this issue at the architectural design level. For example, in [57] a Spatial Transformer Network [22] is employed for character-level HTR, while in [5] an adversarial deformation module is used to warp intermediate convolutional features in a word-level HTR model.…”

Section: Related Workmentioning

confidence: 99%

Boosting modern and historical handwritten text recognition with deformable convolutions

Cascianelli

2022

IJDAR

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Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing style and degradation of the page quality. State-of-the-art HTR approaches typically couple recurrent structures for sequence modeling with Convolutional Neural Networks for visual feature extraction. Since convolutional kernels are defined on fixed grids and focus on all input pixels independently while moving over the input image, this strategy disregards the fact that handwritten characters can vary in shape, scale, and orientation even within the same document and that the ink pixels are more relevant than the background ones. To cope with these specific HTR difficulties, we propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text. We design two deformable architectures and conduct extensive experiments on both modern and historical datasets. Experimental results confirm the suitability of deformable convolutions for the HTR task.

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Grayification: A meaningful grayscale conversion to improve handwritten historical documents analysis

Cited by 11 publications

References 12 publications

Interpol questioned documents review 2019–2022

Interpol questioned documents review 2019–2022

Analysis and development of brain tumor prediction model using deep neural network

Boosting modern and historical handwritten text recognition with deformable convolutions

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