A computer implemented method for automatically selecting one or more algorithms in an image processing environment, the method comprising: receiving a set of images containing at least one image having intra image variations, wherein the intra image variations are linked to one or more k image segments (s1, s2..., sk); segmenting each image of the set of images received into the one or more k image segments (s1, s2..., sk) having numbers of 1 to k; recording the k image segments; performing each of m pre-stored binarization (b 1, b 2..., b m) algorithms upon each of the k image segments (s 1, s 2..., s k) in order to obtain k m binarized image segments, wherein the k m binarized image segments correspond to k m sets of image segments and binarization algorithms; combining the k m binarized image segments into m k binarized images corresponding to a single received image, wherein a single received image generates m k OCR image processing outputs using the m k binarized images and the m k OCR image processing outputs of the single received image are compared to a ground truth to yield a correct or in-correct OCR image processing result, and wherein the correct or in-correct OCR image processing result is used to select each set of image segments and the used binarization algorithms, as a selected training set for a SVM classifier; training the SVM classifier using a feature vector derived from each image segment within each selected training set as an input vector and the binarization algorithm in the selected training set as a desired output; and selecting one or more matching binarization algorithms from one or more training sets for each incoming pre-processed test image, wherein the one or more matching binarization algorithms are selected by matching a feature vector of each test segment of the incoming pre-processed test image and a feature vector associated with the one or more k image segments (s 1, s 2..., s k) recorded, and mapping the matched feature vector using the SVM classifier to the one or more binarization algorithms for providing a corresponding binarized image segment. receiving a set of images containing at least one image having intra image variations, wherein the intra image variations are linked to one or more k image segments (s1, s2..., sk); segmenting each image of the set of images received into the one or more k image segments (s1, s2..., sk) having numbers of 1 to k; recording the k image segments; performing each of m pre-stored binarization (b 1, b 2..., b m) algorithms upon each of the k image segments (s 1, s 2..., s k) in order to obtain k m binarized image segments, wherein the k m binarized image segments correspond to k m sets of image segments and binarization algorithms; combining the k m binarized image segments into m k binarized images corresponding to a single received image, wherein a single received image generates m k OCR image processing outputs using the m k binarized images and the m k OCR image processing outputs of the single received image are compared to a ground truth to yield a correct or in-correct OCR image processing result, and wherein the correct or in-correct OCR image processing result is used to select each set of image segments and the used binarization algorithms, as a selected training set for a SVM classifier; training the SVM classifier using a feature vector derived from each image segment within each selected training set as an input vector and the binarization algorithm in the selected training set as a desired output; and selecting one or more matching binarization algorithms from one or more training sets for each incoming pre-processed test image, wherein the one or more matching binarization algorithms are selected by matching a feature vector of each test segment of the incoming pre-processed test image and a feature vector associated with the one or more k image segments (s 1, s 2..., s k) recorded, and mapping the matched feature vector using the SVM classifier to the one or more binarization algorithms for providing a corresponding binarized image segment.