Fadoua Drira

Document Images Restoration by a New Tensor Based Diffusion Process: Application to the Recognition of Old Printed Documents

A modification of the Weickert coherence enhancing diffusion filter is proposed for which new constraints formulated form the Perona-Malik equation are added. The new diffusion filter, driven by local tensors fields, takes benefit from both of these approaches and avoids problems known to affect them. This filter reinforces character discontinuity and eliminates the inherent problem of corner rounding while smoothing. Experiments conducted on degraded document images illustrate the effectiveness of the proposed method compared to another anisotropic diffusion approaches. A visual quality improvement is thus achieved on these images. Such improvement leads to a noticeable improvement of the OCR systems accuracy proven through the comparison of OCR recognition rates before and after the diffusion process.

A new PDE-based approach for singularity-preserving regularization: application to degraded characters restoration

The massive digitization of heritage documents has raised new prospects for research like degraded document image restoration. Degradations harm the legibility of the digitized documents and limit their processing. As a solution, we propose to tackle the problem of degraded text characters with PDE (partial differential equation)-based approaches. Existing PDE approaches do not preserve singularities and edge continuities while smoothing. Hence, we propose a new anisotropic diffusion by adding new constraints to the Weickert coherence-enhancing diffusion filter in order to control the diffusion process and to eliminate the inherent corner rounding. A qualitative improvement in the singularity preservation is thus achieved. Experiments conducted on degraded document images illustrate the effectiveness of the proposed method compared with other anisotropic diffusion approaches. We illustrate the performance with the study of the optical recognition accuracy rates.

Multiple Learned Dictionaries Based Clustered Sparse Coding for the Super-Resolution of Single Text Image

This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from a suitable dictionary. In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single text image super resolution. For instance, a large High-Resolution/Low-Resolution (HR/LR) patch pair database is collected from a set of high quality character images and then partitioned into several clusters by performing an intelligent clustering algorithm. Two coupled HR/LR dictionaries are learned from each cluster. Based on SC principle, local patch of a LR image is represented from each LR dictionary generating multiple sparse representations of the same patch. The representation that minimizes the reconstruction error is retained and applied to generate a local HR patch from the corresponding HR dictionary. The performance of the proposed approach is evaluated and compared visually and quantitatively to other existing methods applied to text images. In addition, experimental results on character recognition illustrate that the proposed method outperforms the other methods, involved in this study, by providing better recognition rates.

Super-resolution of single text image by sparse representation

This paper addresses the problem of generating a super-resolved text image from a single low-resolution image. The proposed Super-Resolution (SR) method is based on sparse coding which suggests that image patches can be well represented as a sparse linear combination of elements from a suitably chosen learned dictionary. Toward this strategy, a High-Resolution/Low-Resolution (HR/LR) patch pair data base is collected from high quality character images. To our knowledge, it is the first generic database allowing SR of text images may be contained in documents, signs, labels, bills, etc. This database is used to train jointly two dictionaries. The sparse representation of a LR image patch from the first dictionary can be applied to generate a HR image patch from the second dictionary. The performance of such approach is evaluated and compared visually and quantitatively to other existing SR methods applied to text images. In addition, we examine the influence of text image resolution on automatic recognition performance and we further justify the effectiveness of the proposed SR method compared to others.

Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection

Resolution enhancement has become a valuable research topic due to the rapidly growing need for high-quality images in various applications. Various resolution enhancement approaches have been successfully applied on natural images. Nevertheless, their direct application to textual images is not efficient enough due to the specificities that distinguish these particular images from natural images. The use of insufficient resolution introduces substantial loss of details which can make a text unreadable by humans and unrecognizable by OCR systems. To address these issues, a sparse coding-based approach is proposed to enhance the resolution of a textual image. Three major contributions are presented in this paper: (1) Multiple coupled dictionaries are learned from a clustered database and selected adaptively for a better reconstruction. (2) An automatic process is developed to collect the training database, which contains writing patterns extracted from high-quality character images. (3) A new local feature descriptor well suited for writing specificities is proposed for the clustering of the training database. The performance of these propositions is evaluated qualitatively and quantitatively on various types of low-resolution textual images. Significant improvements in visual quality and character recognition rates are achieved using the proposed approach, confirmed by a detailed comparative study with state-of-the-art upscaling approaches.

Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution

Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate dual dictionaries representing coupled feature spaces. Under this approach, we optimize the training of a first dictionary for the high-resolution image space and then a second dictionary is simply deduced from the latter for the low-resolution image space. In contrast with the classical dictionary learning approaches, the proposed approach allows a noticeable speedup and a major simplification of the coupled dictionary learning phase both in terms of algorithm architecture and computational complexity. Furthermore, the resolution enhancement results achieved by applying the proposed approach on poorly resolved textual images lead to image quality improvements.

Denoising Textual Images Using Local/Non-local Smoothing Filters: A Comparative Study

Textual document image denoising is the main issue of this work. Therefore, we introduce a comparative study between two state-of-the-art denoising frameworks : local and non-local smoothing filters. The choice of both of these frameworks is directly related to their ability to deal with local data corruption and to process oriented patterns, a major characteristic of textual documents. Local smoothing filters incorporate anisotropic diffusion approaches where as non-local filters introduce non-local means. Experiments conducted on synthetic and real degraded document images illustrate the behaviour of the studied frameworks on the visual quality and even on the optical recognition accuracy rates.

Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding

In this paper, we propose a new approach based on sparse coding for single textual image Super-Resolution (SR). The proposed approach is able to build more representative dictionaries learned from a large training Low-Resolution/High-Resolution (LR/HR) patch pair database. In fact, an intelligent clustering is employed to partition such database into several clusters from which multiple coupled LR/HR dictionaries are constructed. Based on the assumption that patches of the same cluster live in the same subspace, we exploit for each local LR patch its similarity to clusters in order to adaptively select the appropriate learned dictionary over that such patch can be well sparsely represented. The obtained sparse representation is hence applied to generate a local HR patch from the corresponding HR dictionary. Experiments on textual images show that the proposed approach outperforms its counterparts in visual fidelity as well as in numerical measures.

Fast Integral MeanShift: Application to Color Segmentation of Document Images

Global Mean Shift algorithm is an unsupervised clustering technique already applied for color document image segmentation. Nevertheless, its important computational cost limits its application for document images. The complexity of the global approach is explained by the intensive search of colors samples in the Parzen window to compute the vector oriented toward the mean. For making it more flexible, several attempts have tried to decrease the algorithm complexity mainly by adding spatial information or by reducing the number of colors to shift or even by selecting a reduced number of colors to estimate the means of density function. This paper presents a fast optimized Mean Shift with a much reduced computational cost. This algorithm uses both the discretisation of the shift and the integral image which allow the computation of means into the Parzen windows with a reduced and fixed number of operations. With the discretisation of the color space, the fast optimised MeanShift also memorizes all existing paths to avoid shifting again colors along similar path. Despite the square shape of the Parzen windows and the uniform kernel used, the results are very similar to those obtained by the global Mean Shift algorithm. The proposed algorithm is compared to the different existing implementation of similar algorithms found in the literature.

OCR Accuracy Improvement through a PDE-Based Approach

This paper focuses on improving the optical character recognition (OCR) system s accuracy by restoring damaged character through a PDE (Partial Differential Equation)-based approach. This approach, proposed by D. Tschumperle, is an anisotropic diffusion approach driven by local tensors fields. Actually, such approach has many useful properties that are relevant for use in character restoration. For instance, this approach is very appropriate for the processing of oriented patterns which are major characteristics of textual documents. It incorporates both edge enhancing diffusion that tends to preserve local structures during smoothing and coherence-enhancing diffusion that processes oriented structures by smoothing along the flow direction. Furthermore, this tensor diffusion-based approach compared to the existing sate of the art requires neither segmentation nor training steps. Some experiments, done on degraded document images, illustrate the performance of this PDE-based approach in improving both of the visual quality and the OCR accuracy rates for degraded document images.