Restoration based Contourlet Transform for historical document image binarization

Historical Document Image Binarization: A Review

“…Roe and Mello [140] proposed an interesting variation on a bilateral filter, where each pixel is set to the median of its neighbors that have sufficient color similarity. Other low-pass filtering is done by truncating high frequency information through image transforms such as the contourlet transform [177] and a Gabor wavelet decomposition [102].…”

“…dev. AdOtsu [95] Local threshold Local version of Otsu Lu [82] Edge based Local thresholding near edges after background removal Su [162] Edge based Filters canny edges using local contrast Jia [53] Edge based Detecting symmetry of stroke edges Valizadeh [166] Edge based Adaptive water flow model Hadjadj [43] Edge based Active contours initialized using contrast edges [162] Rivest [139] Edge based Level-set method Nafchi [104] Image transform Threshold filter response in frequency domain Sehad [147] Image transform Performs background removal using Fourier Transform Zemouri [177] Image transform Uses Contourlet Transform to smooth image FAIR [73] Mixture model Ensemble of MoG with post-filtering Hedjam [47] Mixture model MoG with spatially varying Σ k Mishra [91] Mixture model 10-component MoG for foreground color variation Mitainoudis [93] Mixture model MoG over pairs of intensity co-occurrences Ramirez [138] Mixture model Mixture of log-normal distributions outperforms MoG Howe [50] CRF Laplacian unary term and pairwise Canny-based term Ayyalasomayajula [7] CRF Pairwise terms based on an initial binarization Peng [120] CRF Pairwise terms based on an initial foreground skeleton Su [161] CRF Uses CRF to classify uncertain pixels Ahmadi [2] CRF Learns linear combination of feature functions GiB [13] Game theory Extracts features for clustering using game theory Hamza [44] Shallow ML Self-organizing map to cluster pixels Rabelo [132] Shallow ML MLP to classify pixels using local mean Kefali [58] Shallow ML MLP using local intensities and global statistic features Pastor [118] Shallow ML MLP with F-measure loss function Kasmin [56] Shallow ML Ensemble of 8 SVMs Wu [174] Shallow ML Random forest trained on a rich feature set Pastor [119] Deep learning First CNN for binarization Peng [121] Deep learning Encoder-decoder FCN trained on synthetic data Calvo-Zaragoza ...…”

“…The Contourlet transform is composed of a Laplacian pyramid decomposition to obtain multi-scale analysis, and a directional filter bank is applied at each scale [177]. The resulting contourlet coefficients give a more concentrated representation of the image edges, which are thresholded using Niblack.…”

Improved wolf algorithm on document images detection using optimum mean technique

“…Recently, Document Image Binarization (DIB) is one of the first stages of the Document Image Analysis (DIA) and the further recognition pipeline. It consists mainly of classifying the pixels of the image into the background and foreground pixels [1][2][3][4]. At this stage, "binarization" will imply some cleaning and enhancement, since detecting the foreground pixel will remove, for instance, ink spots, degradations or inkbleed through, besides it will recover lost strokes or other foreground information.…”