150 research outputs found
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
The efficient segmentation of foreground text information from the background
in degraded color document images is a hot research topic. Due to the imperfect
preservation of ancient documents over a long period of time, various types of
degradation, including staining, yellowing, and ink seepage, have seriously
affected the results of image binarization. In this paper, a three-stage method
is proposed for image enhancement and binarization of degraded color document
images by using discrete wavelet transform (DWT) and generative adversarial
network (GAN). In Stage-1, we use DWT and retain the LL subband images to
achieve the image enhancement. In Stage-2, the original input image is split
into four (Red, Green, Blue and Gray) single-channel images, each of which
trains the independent adversarial networks. The trained adversarial network
models are used to extract the color foreground information from the images. In
Stage-3, in order to combine global and local features, the output image from
Stage-2 and the original input image are used to train the independent
adversarial networks for document binarization. The experimental results
demonstrate that our proposed method outperforms many classical and
state-of-the-art (SOTA) methods on the Document Image Binarization Contest
(DIBCO) dataset. We release our implementation code at
https://github.com/abcpp12383/ThreeStageBinarization
Ancient Documents Denoising and Decomposition Using Aujol and Chambolle Algorithm
With the improvement of printing technology since the 15th century, there is a huge amount of printed documents published and distributed. These documents are degraded by the time and require to be preprocessed before being submitted to image indexing strategy, in order to enhance the quality of images. This paper proposes a new pre-processing that permits to denoise these documents, by using a Aujol and Chambolle algorithm. Aujol and Chambolle algorithm allows to extract meaningful components from image. In this case, we can extract shapes, textures and noise. Some examples of specific processings applied on each layer are illustrated in this paper
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization
To efficiently extract the textual information from color degraded document
images is an important research topic. Long-term imperfect preservation of
ancient documents has led to various types of degradation such as page
staining, paper yellowing, and ink bleeding; these degradations badly impact
the image processing for information extraction. In this paper, we present
CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete
wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The
proposed method comprises three stages: image preprocessing, image enhancement,
and image binarization. This work conducts comparative experiments in the image
preprocessing stage to determine the optimal selection of DWT with
normalization. Additionally, we perform an ablation study on the results of the
image enhancement stage and the image binarization stage to validate their
positive effect on the model performance. This work compares the performance of
the proposed method with other state-of-the-art (SOTA) methods on DIBCO and
H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The
experimental results demonstrate that CCDWT-GAN achieves a top two performance
on multiple benchmark datasets, and outperforms other SOTA methods
Restoration of deteriorated text sections in ancient document images using atri-level semi-adaptive thresholding technique
The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text
Illumination removal and text segmnetation for Al-Quran using binary representation
Segmentation process for segmenting Al-Quran needs to be studied carefully. This is because Al-Quran is the book of Allah swt. Any incorrect segmentation will affect the holiness of Al-Quran. A major difficulty is the appearance of illumination around text areas as well as of noisy black stripes. In this study, we propose a novel algorithm for detecting the illumination on Al-Quran page. Our aim is to segment Al-Quran pages to pages without illumination, and to segment Al-Quran pages to text line images without any changes on the content. First we apply a pre-processing which includes binarization. Then, we detect the illumination of Al-Quran pages. In this stage, we introduce the vertical and horizontal white percentages which have been proved efficient for detecting the illumination. Finally, the new images are segmented to text line. The experimental results on several Al-Quran pages from different Al-Quran style demonstrate the effectiveness of the proposed technique
A new approach for centerline extraction in handwritten strokes: an application to the constitution of a code book
International audienceWe present in this paper a new method of analysis and decomposition of handwritten documents into glyphs (graphemes) and their associated code book. The different techniques that are involved in this paper are inspired by image processing methods in a large sense and mathematical models implying graph coloring. Our approaches provide firstly a rapid and detailed characterization of handwritten shapes based on dynamic tracking of the handwriting (curvature, thickness, direction, etc.) and also a very efficient analysis method for the categorization of basic shapes (graphemes). The tools that we have produced enable paleographers to study quickly and more accurately a large volume of manuscripts and to extract a large number of characteristics that are specific to an individual or an era
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