Text Extraction and Restoration of Old Handwritten Documents
- URL: http://arxiv.org/abs/2001.08742v1
- Date: Thu, 23 Jan 2020 05:42:39 GMT
- Title: Text Extraction and Restoration of Old Handwritten Documents
- Authors: Mayank Wadhwani, Debapriya Kundu, Deepayan Chakraborty, Bhabatosh
Chanda
- Abstract summary: This paper describes two novel methods for the restoration of old degraded handwritten documents using deep neural network.
A small-scale dataset of 26 heritage letters images is introduced.
Experiments demonstrate that the proposed systems perform well on handwritten document images with severe degradations.
- Score: 3.514869837986596
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image restoration is very crucial computer vision task. This paper describes
two novel methods for the restoration of old degraded handwritten documents
using deep neural network. In addition to that, a small-scale dataset of 26
heritage letters images is introduced. The ground truth data to train the
desired network is generated semi automatically involving a pragmatic
combination of color transformation, Gaussian mixture model based segmentation
and shape correction by using mathematical morphological operators. In the
first approach, a deep neural network has been used for text extraction from
the document image and later background reconstruction has been done using
Gaussian mixture modeling. But Gaussian mixture modelling requires to set
parameters manually, to alleviate this we propose a second approach where the
background reconstruction and foreground extraction (which which includes
extracting text with its original colour) both has been done using deep neural
network. Experiments demonstrate that the proposed systems perform well on
handwritten document images with severe degradations, even when trained with
small dataset. Hence, the proposed methods are ideally suited for digital
heritage preservation repositories. It is worth mentioning that, these methods
can be extended easily for printed degraded documents.
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