Text enhancement by PDE's based methods

  • Authors:
  • Zouhir Mahani;Jalal Zahid;Sahar Saoud;Mohammed El Rhabi;Abdelilah Hakim

  • Affiliations:
  • ESTA, Laboratoire Matriaux, Systémes et Technologies de l'information, Université Ibn Zohr, Agadir, Morocco;ESTA, Laboratoire Matriaux, Systémes et Technologies de l'information, Université Ibn Zohr, Agadir, Morocco;ESTA, Laboratoire Matriaux, Systémes et Technologies de l'information, Université Ibn Zohr, Agadir, Morocco;Faculté des Sciences et Techniques - Guéliz (FSTG), Laboratoire de Mathématiques Appliquées et Informatique, Université Cadi Ayyad, Guéliz, Marrakech, Morocco;Faculté des Sciences et Techniques - Guéliz (FSTG), Laboratoire de Mathématiques Appliquées et Informatique, Université Cadi Ayyad, Guéliz, Marrakech, Morocco

  • Venue:
  • ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
  • Year:
  • 2012

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Abstract

In this work, we propose a new method to enhance text in document-image. Firstly, we introduce a classical model and a way to solve it by means of a non-convex optimization problem. So, a simoultaneaous estimation of the reflectance and the luminance is obtained when the non uniform illumination (also called luminance) is a smooth function and the reflectance is a function of bounded variation. We give an analyse of this problem and some conditions of existence and unicity. Then, we consider the "log" of the classical model. A new pde's model is proposed. This method is based on the resolution of an original partial differential equation (PDE) estimating the log of the luminance. We assume that the luminance is enough smooth and is the solution of a non classical second order's PDE.Then we deduce the reflectance from the estimated luminance and the acquired image. The effectiveness and the robustness of the proposed process are shown on numerical examples in real-world situation (images acquired from cameraphones). Then, we illustrate the ability of this method to improve an Optical Character Recognition (OCR) in text recognition.