An MRF Model for Binarization of Natural Scene Text
Summary (1 min read)
Summary
- Over the past two decades, numerous scientific studies have demonstrated that endocrine disrupting 70 chemicals (EDCs) elicit adverse effects on sensitive aquatic species, such as fish [1-7].
- The REP, in turn, is the ratio of the effect 222 concentration of the reference compound estradiol EC50(E2) and the chemical i’s EC50(i) (Equation 2).
- This result is supported by two recent reviews on the performance of current analytical 306 methods that have shown that 35 % of reviewed methods complied with the EQS for E2, while only one 307 method complied with the EQS for EE2 [49, 50].
- The situation for MELN is markedly different from that of ER-CALUX.
- 376 Thus, EEQchem results for MELN are strongly based on E1 concentrations – a compound that was always 377 measured (except for a few samples by Lab 2, Figure 3).
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Citations
5,311 citations
Cites methods from "An MRF Model for Binarization of Na..."
...Finally, we note that in prior work binarization has been an important component in scene text applications, driven partly by efforts to re-use existing OCR machinery in new domains [24, 25]....
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789 citations
709 citations
Cites methods from "An MRF Model for Binarization of Na..."
...segmentation problems, Mishra [135] and Kim and Lee [185] formulated the text binarization problem in optimal frameworks and used an energy minimization to label text pixels....
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...Mishra et al. [161] presented a framework that utilizes both bottom-up (character) and top-down (language) cues for text recognition....
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...Inspired by the success of CRF models for solving image segmentation problems, Mishra [135] and Kim and Lee [185] formulated the text binarization problem in optimal frameworks and used an energy minimization to label text pixels....
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303 citations
Cites methods from "An MRF Model for Binarization of Na..."
...We intentionally avoid the term “character detection” as certain algorithms (such as [17, 29]) utilize binarization to seek character candidates....
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...However, binarization based methods [17, 29] are sensitive to noise, blur and nonuniform illumination; connected component based methods [21, 23] are unable to handle connected characters and...
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...To tackle these issues, several approaches were proposed, which employed adaptive binarization [17, 29], connected component extraction [21, 23] or direct character detection [27, 18, 25]....
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243 citations
References
37,017 citations
"An MRF Model for Binarization of Na..." refers methods in this paper
...We also compare our method with Otsu followed by colour thresholding (CT) [14]....
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...Otsu followed by colour thresholding binarization proposed in [14] improves the word recognition accuracy but not significantly....
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...Traditional thresholding based binarization can be categorized into two categories: the one which uses global threshold for the given document (like Otsu [5], Kittler et al. [6]) and the one with local thresholds (like Sauvola [7], Niblack [8])....
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...To evaluate the performance of proposed binarization algorithm, we compare it with the well-known thresholding based binarization techniques like Otsu [5], Sauvola [7], Niblack [8], Kittler et al. [6]....
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...Since this dataset consists of images of tight word boundaries, global methods (like [5], [6]) performs better than popular local methods....
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5,670 citations
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"An MRF Model for Binarization of Na..." refers background or methods in this paper
...But these methods lack a principled formulation of the binarization problem of complex colour documents, and hence can not be generalized....
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...Then, based on the data and smoothness terms in Equation (5) and (6) respectively, the graph is formed....
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...All the implementations of the proposed method are done using C++ graph cut code [15] and Matlab....
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Related Papers (5)
Frequently Asked Questions (12)
Q2. What is the way to make the binarization process more efficient?
iterative graph cut based binarization is also more suitable for their application as it refines seeds and, binarization output at each iteration and thus produces a clean binarization result even in case of noisy foreground/background distributions.
Q3. What is the energy function in Equation (1)?
Due to the introduction of GMMS the energy function in Equation (1) now becomes:E(x, k, θ, z) = Ei(x, k, θ, z) + Eij(x, z), (4)i.e. the data term depends on its assignment to GMM component.
Q4. How long does it take to produce a binary image?
The proposed method takes 32 seconds on average to produce final binary result for an image on system with 2 GB RAM and Intel R© CoreTM 2 Duo CPU with 2.93 GHz processor system.
Q5. What is the meaning of edginess difference?
(Note that by edginess difference term the authors mean, energy function with gradient magnitude difference in addition to difference in RGB colour space).
Q6. What is the common term used in the literature?
the smoothness term most commonly used in literature is the Potts model:Eij(x, z) = λ ∑(i,j)∈N exp−(zi − zj)2 2β2 [xi = xj ] dist(i, j) ,where λ determines the degree of smoothness, dist(i, j) is the Euclidean distance between neighbouring pixels i and j.
Q7. What is the gradient orientation of the edge pixel?
For every such edge pixel p the authors traverse the edge image in direction of θ until the authors hit an edge pixel q whose gradient orientation is (π−θ)± π36 (i.e. approximately opposite gradient direction).
Q8. how to make the energy function robust to low contrast colour images?
(5)In order to make the energy function robust to low contrast colour images the authors modify the smoothness term of the energy function by adding a new term which measures the “edginess” of the pixels as follows:Eij(x, z) = λ1 ∑(i,j)∈N [xi = xj ]exp(−β||zi − zj||2)+λ2 ∑(i,j)∈N [xi = xj ]exp(−β||wi − wj ||2).
Q9. What is the way to get the pixel colour from a GMMRF?
ITERATIVE GRAPH CUT BASED BINARIZATIONIn GMMRF framework [4], each pixel colour is generated from one of the 2c Gaussian Mixture Models (GMMS) (c GMMS for foreground and background each) with mean μ and covariance Σ i.e. each foreground colour pixel is generated from following distribution:p(zi|xi, θ, ki) = N (z, θ; μ(xi, ki), Σ(xi, ki)), (3) where N denotes a Gaussian distribution, xi ∈ {0, 1} and ki ∈ {1, ..., c}.
Q10. What are the main problems of the previous binarization algorithms?
Although most of these previous algorithms perform satisfactorily for many cases, they suffer from the problems like: (1) Manual tuning of parameters, (2) High sensitivity to the choice of parameters, (3) Handling images with uneven lighting, noisy background, similar foreground-background colours.
Q11. How do the authors determine the background of the graph?
The authors then re-estimate GMMS using an initial binarization result and iterate the graph cut over new data and smoothness term, until convergence.
Q12. What is the main difference between the two methods?
But these methods lack a principled formulation of the binarization problem of complex colour documents, and hence can not be generalized.