Doc-Attentive-GAN: attentive GAN for historical document denoising

Neji, Hala; Ben Halima, Mohamed; Nogueras-Iso, Javier; Hamdani, Tarek M.; Lacasta, Javier; Chabchoub, Habib; Alimi, Adel M.

doi:10.1007/s11042-023-17476-2

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…In order to overcome these issues, a previous text normalization stage has been established as common practice for most HTR algorithms. This preprocessing step is in consonance with the classic methodology in many types of computer vision problems: the images have to be normalized before the subsequent recognition models are applied [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Classical solutions were based on heuristics algorithms [ 9 , 10 ]. Some authors omitted this normalization process [ 11 , 12 ] with mixed results, but those who have applied normalization have typically obtained as good and usually better results with similar recognition models [ 5 , 6 , 13 , 14 , 15 , 16 ]. In recent years, many authors have researched preprocessing tasks, but most of them were focused only on one of the substeps mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…Some works within the literature deal with the use of GAN for image normalization purposes [ 19 ]. In the area of automatic document analysis, GAN models have been used to delete background and printed text elements [ 20 ], denoise [ 5 ], deblur [ 21 ] or binarize [ 22 ] the images, among others. Some GAN models also normalize the text width with erosion and dilation transformations [ 23 ] or try to remove the noise present in document images [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization

Barreiro-Garrido,

Ruiz-Parrado,

Moreno

et al. 2024

Sensors

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In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture.

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Section: Introductionmentioning

confidence: 99%