Ergina Kavallieratou

Improving the quality of degraded document images

It is common for libraries to provide public access to historical and ancient document image collections. It is common for such document images to require specialized processing in order to remove background noise and become more legible. In this paper, we propose a hybrid binarization approach for improving the quality of old documents using a combination of global and local thresholding. First, a global thresholding technique specifically designed for old document images is applied to the entire image. Then, the image areas that still contain background noise are detected and the same technique is re-applied to each area separately. Hence, we achieve better adaptability of the algorithm in cases where various kinds of noise coexist in different areas of the same image while avoiding the computational and time cost of applying a local thresholding in the entire image. Evaluation results based on a collection of historical document images indicate that the proposed approach is effective in removing background noise and improving the quality of degraded documents while documents already in good condition are not affected

An unconstrained handwriting recognition system

Abstract. In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printed-handwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.

Skew angle estimation for printed and handwritten documents using the Wigner–Ville distribution

A skew estimation algorithm for printed and handwritten documents, based on the document’s horizontal projection profile and its Wigner – Ville distribution, is presented. The proposed algorithm is able to correct skew angles that range between 289 and þ898 detecting the right oriented position of the page by the alternations of the horizontal projection profile. It is able of processing successfully handwritten documents, even if they consist of non-parallel text lines. It deals with the presence of graphics, while a few text lines suffice for the application of the algorithm. Furthermore, the latter permits the use of only a part of the page for the skew estimation minimizing the computational complexity. The proposed algorithm was evaluated on a wide variety of pages (i.e. printed, handwritten, multi-column, application forms etc.) achieving a success rate of 100% within a confidence range of ^ 0.38. q 2002 Published by Elsevier Science B.V.

A binarization algorithm specialized on document images and photos

In this paper, a new method for document images or photos binarization is presented. The method is simple, fast and robust and appropriate for normal as well as for special cases of documents like photos, historical documents etc. The proposed method is applied to problematic cases of documents and it is compared to other traditional methods.

AN INTEGRATED SYSTEM FOR HANDWRITTEN DOCUMENT IMAGE PROCESSING

In this paper we attempt to face common problems of handwritten documents such as nonparallel text lines in a page, hill and dale writing, slanted and connected characters. Towards this end an integrated system for document image preprocessing is presented. This system consists of the following modules: skew angle estimation and correction, line and word segmentation, slope and slant correction. The skew angle correction, slope correction and slant removing algorithms are based on a novel method that is a combination of the projection profile technique and the Wigner–Ville distribution. Furthermore, the skew angle correction algorithm can cope with pages whose text line skew angles vary, and handle them by areas. Our system can be used as a preprocessing stage to any handwriting character recognition or segmentation system as well as to any writer identification system. It was tested in a wide variety of handwritten document images of unconstrained English and Modern Greek text from about 100 writers. Add...

An evaluation survey of binarization algorithms on historical documents

Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In this paper, we try to answer the question ldquohow well an existing binarization algorithm can binarize a degraded document image?rdquo We propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesnpsilat require anything more than the binarization stage. Then we apply the proposed technique to 30 existing binarization algorithms. Experimental results and conclusions are presented.

Discrimination of machine-printed from handwritten text using simple structural characteristics

In this paper, we present a trainable approach to discriminate between machine-printed and handwritten text. An integrated system able to localize text areas and split them in text-lines is used. A set of simple and easy-to-compute structural characteristics that capture the differences between machine-printed and handwritten text-lines is introduced. Experiments on document images taken from IAM-DB and GRUHD databases show a remarkable performance of the proposed approach that requires minimal training data.

Skew angle estimation in document processing using Cohen's class distributions

Abstract A skew angle estimation approach based on the application of several time–frequency distributions of Cohens class to the horizontal projection profile of the page is proposed for document processing. Our results prove that the Wigner–Ville is the best trade-off between accuracy and computational cost.

Adaptive Binarization of Historical Document Images

In this paper, we present a binarization technique specifically designed for historical document images. Existing methods for this problem focus on either finding a good global threshold or adapting the threshold for each area to remove smear, strains, uneven illumination etc. We propose a hybrid approach that first applies a global thresholding method and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. We evaluate the proposed approach for different kinds of degradation problems. The results show that our method can handle hard cases while documents already in good condition are not affected drastically

Handwritten word recognition based on structural characteristics and lexical support

In this paper a handwritten recognition algorithm based on structural characteristics, histograms and profiles, is presented. The well-known horizontal and vertical histograms are used, in combination with the newly introduced radial histogram, out-in radial and in-out radial profiles for representing 32 /spl times/ 32 matrices of characters, as 280-dimension vectors. The recognition process has been supported by a lexical component based on dynamic acyclic FSAs (Finite-State-Automata).