A Comprehensive Survey on Image Binarization Techniques

Chaki, Nabendu; Shaikh, Soharab Hossain; Saeed, Khalid

doi:10.1007/978-81-322-1907-1_2

Cited by 64 publications

(37 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the number of peaks is less than or equal to 2, then the image is considered noiseless, otherwise it is noisy. This decision is inspired from the work in [3].…”

Section: A Decision By Sharp-peakmentioning

confidence: 99%

“…Moghaddam and Cheriet [1] proposed a method, which starts with a large window size and iteratively reduces it to a proper window size. Our method uses the information provided by the sharp-Peak algorithm reported in [3] to automatically decide on the type of the image and hence, based on the experimental observations, we fix the window size to 40 × 40 and 100 × 100 pixels for noisy and noiseless images, respectively. Figure 1(d) shows the effect resulting from the estimation of the background by the proposed method applied to the image in Fig.…”

Section: B Background Estimationmentioning

confidence: 99%

“…Some of these effects are faded ink, bleed-through, show-through, uneven illumination, variations in image contrast [1], [2]. A large number of document image binarization techniques [3] based on different approaches have been proposed in the literature to solve the old document image binarization problems mentioned above. Almost all the existing binarization techniques are complex and compounded from filters and signal processing operations [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Khitas¹,

Ziet²,

Bouguezel³

2018

ElAEE

View full text Add to dashboard Cite

In this paper, we propose a new binarization method suitable for images having a variety of sizes and degradation levels. It is mainly based on the idea of estimating a document background surface by new smoothing approach through a median filter and compute-Sharp-Peak to replace iterative polynomial. The resulting document image is then segmented by a global threshold binarization. The simulation results confirm that the performance of the proposed method is generally competitive to that of the existing methods. For highly degraded document images, specifically the documents in the BICKLEY DIARY data-base, the performance of the former is substantially better than that of the latter. We show how this approach outperforms the existing and widely used binarization methods in terms of accuracy, F-measure, PSNR, NRM, MPM, DRD, recall and precision.

show abstract

“…If the number of peaks is less than or equal to 2, then the image is considered noiseless, otherwise it is noisy. This decision is inspired from the work in [3].…”

Section: A Decision By Sharp-peakmentioning

confidence: 99%

Section: B Background Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Khitas¹,

Ziet²,

Bouguezel³

2018

ElAEE

View full text Add to dashboard Cite

show abstract

“…[1][2] The first step to improve the performance of OCR systems is preprocessing the input scene using binarization, segmentation, skew detection, dewarping and image improving algorithms. [3][4][5] Unfortunately, there is no unique effective preprocessing for degraded input images; that is, the processing depends on many degradation and capture parameters to be estimated. Character recognition is a well-studied problem.…”

Section: Introductionmentioning

confidence: 99%

Optical character recognition of camera-captured images based on phase features

Diaz-Escobar

Kober

2015

Applications of Digital Image Processing XXXVIII

View full text Add to dashboard Cite

Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

show abstract

“…1 The preprocessing module is typically performing noise removal and binarization tasks. 11,12 The representation module, in most cases, provides the system with a set of contours or skeletons from a given image. 1 Most of the methods in the literature can then be coarsely divided into two categories based on the way the output of the representation module is treated: in the so-called category of segmentation-based approaches, a word or a text-line image is first segmented into characters (or strokes or similar units), and then further recognition processing is applied on each segmented character.…”

Section: Introductionmentioning

confidence: 99%

A comparison of 1D and 2D LSTM architectures for the recognition of handwritten Arabic

et al. 2015

View full text Add to dashboard Cite

In this paper, we present an Arabic handwriting recognition method based on recurrent neural network. We use the Long Short Term Memory (LSTM) architecture, that have proven successful in different printed and handwritten OCR tasks. Applications of LSTM for handwriting recognition employ the two-dimensional architecture to deal with the variations in both vertical and horizontal axis. However, we show that using a simple pre-processing step that normalizes the position and baseline of letters, we can make use of 1D LSTM, which is faster in learning and convergence, and yet achieve superior performance. In a series of experiments on IFN/ENIT database for Arabic handwriting recognition, we demonstrate that our proposed pipeline can outperform 2D LSTM networks. Furthermore, we provide comparisons with 1D LSTM networks trained with manually crafted features to show that the automatically learned features in a globally trained 1D LSTM network with our normalization step can even outperform such systems.

show abstract

A Comprehensive Survey on Image Binarization Techniques

Cited by 64 publications

References 23 publications

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Optical character recognition of camera-captured images based on phase features

A comparison of 1D and 2D LSTM architectures for the recognition of handwritten Arabic

Contact Info

Product

Resources

About