In this paper we explore a set of modifications of the cascade structure of the Viola-Jones detector on the example of solving stamp detection problem. The experiments on the public “SPODS” dataset for various document attributes extraction problems with extremely limited training set are presented. The positive training set is augmented by applying various image processing algorithms relevant to the stamp model to an available in a single instance image for each stamp type. We describe and analyze such structures of the Viola and Jones classifiers as the original cascade structure, tree, soft cascade, and perform the training experiments. Experimental results show that each modification of the cascade structure of the classifier has its own advantages and disadvantages, and the choice of the Viola-Jones classifier design significantly affects the quality of solving object detection problem.
Recognition of identity documents using mobile devices has become a topic of a wide range of computer vision research. The portfolio of methods and algorithms for solving such tasks as face detection, document detection and rectification, text field recognition, and other, is growing, and the scarcity of datasets has become an important issue. One of the openly accessible datasets for evaluating such methods is MIDV-500, containing video clips of 50 identity document types in various conditions. However, the variability of capturing conditions in MIDV-500 did not address some of the key issues, mainly significant projective distortions and different lighting conditions. In this paper we present a MIDV-2019 dataset, containing video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. The description of the added data is presented, and experimental baselines for text field recognition in different conditions.
In this paper we present a single-sample augmentation framework. The key idea of the framework consists of synthesizing a positive training set from a single natural sample using relevant geometric and pixel intensity transforms. The efficiency of the proposed framework has been demonstrated solving round seal stamp detection problem using Viola-Jones approach on the public “SPODS” dataset. The mentioned image transformations make it possible to simulate different orientation of the stamps, color differences, and distortions caused by stamping process and document aging. The proposed framework can be applied to training various machine learning algorithms for solving computer vision and computed tomography problems.
In this paper we study combination of Viola-Jones classifier with deep convolutional neural network as an approach to the problem of object detection and classification. It is well known that Viola-Jones detectors are fast and accurate in detection of vast variety of different objects. On the other hand, methods based on neural network usage demonstrate high accuracy in the problems of image classification. The main goal of this paper is to study viability of Viola-Jones classifier in problem of image classification. The first part of both algorithms is the same: we will use Viola-Jones classifier to find object bounding rectangle in the image. The second part of the algorithms is different: we will compare usage of Viola-Jones classifier with convolutional neural network-based classifier. We will provide speed and accuracy comparison between these two algorithms.
In this paper we present modification of the Viola-Jones approach for solving government seal stamp of the Russian Federation detection problem. The main contributions of the proposed modification are combining brightness and edge features as well as using L1 norm of the gradient of the image for calculating edge features. This modification allows to build classifiers which are more robust to noise, absence of a characteristic structure of contrasts and object's boundaries. The modification is experimentally compared to original Viola-Jones algorithm and showing better quality on different testing sets.