We describe a method for defect detection and classification for collections of digital images of historical book documents. Undistorted text images from various books characterized by strong variation of language, font and layout properties are discriminated from typical errors in digitization processes such as occlusion by an operator's hand, visible book edge or image warping artifacts. A bag of local features approach is compared to a global characterization of location, size and orientation properties of detected keypoints. Machine learning is used to discriminate between those classes. Results for different features are compared for the task of discrimination between undistorted text and the major distortion class which is presence of the operator's hand, where features based on the bag of local features derived histograms achieved a cross-validation accuracy better than 99 percent on a representative data set. Taking into account up to three classes of distortions still resulted in cross-validation accuracies beyond 90 percent using bag of local features derived visual histograms for classifier input.