Estimation of the Handwritten Text Skew Based on Binary Moments

Binary moments represent one of the methods for the text skew estimation in binary images. It has been used widely for the skew identification of the printed text. However, the handwritten text consists of text objects, which are characterized with different skews. Hence, the method should be adapted for the handwritten text. This is achieved with the image splitting into separate text objects made by the bounding boxes. Obtained text objects represent the isolated binary objects. The application of the moment-based method to each binary object evaluates their local text skews. Due to the accuracy, estimated skew data can be used as an input to the algorithms for the text line segmentation

LANGUAGE INDEPENDENT ROBUST SKEW DETECTION AND CORRECTION TECHNIQUE FOR DOCUMENT IMAGES

Document image processing is an increasingly important technology essential in all optical character recognition (OCR) systems and for automation of various office documents. A document originally has zero-skew (tilt), but when a page is scanned or photo copied, skew may be introduced due to various factors and is practically unavoidable. Presence even a small amount of skew (0.50) will have detrimental effects on document analysis as it has a direct effect on the reliability and efficiency of segmentation, recognition and feature extraction stages. Therefore removal of skew is of paramount importance in the field of document analysis and OCR and is the first step to be accomplished. This paper presents a novel technique for skew detection and correction which is both language and content independent. The proposed technique is based on the maximum density of the foreground pixels and their orientation in the document image. Unlike other conventional algorithms which work only for machine printed textual documents scripted in English, this technique works well for all kinds of document images (machine printed, hand written, complex, noisy and simple). The technique presented here is tested with 150 different document image samples and is found to provide results with an accuracy of 0.1

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

2x1D Image Registration and Comparison

This paper presents a novel 2x1D phase correlation based image registration method for verification of printer emulator output. The method combines the basic phase correlation technique and a modified 2x1D version of it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing with diverse images with large translations and image cropping

Statistics Oriented Preprocessing of Document Image

Old printed documents represent an important part of our cultural heritage. Their digitalization plays an important role in creating data and metadata. The paper proposed an algorithm for estimation of the global text skew. First, document image is binarized reducing the impact of noise and uneven illumination. The binary image is statistically analyzed and processed. Accordingly, redundant data have been excluded. Furthermore, the convex hulls are established encircling each text object. They are joined establishing connected components. Then, the connected components in complementary image are enlarged with morphological dilation. At the end, the biggest connected component is extracted. Its orientation is similar to the global orientation of text document which is calculated by the moments. Efficiency and correctness of the algorithm are verified by testing on a custom dataset

Image Segmentation and Multiple skew estimation, correction in printed and handwritten documents

Analysis of handwritten document has always been a challenging task in the field of image processing. Various algorithms have been developed in finding solution to this problem. The algorithms implemented here for segmentation and skew detection works not only on printed or scanned document images but for also handwritten document images which creates an edge over other methodologies. Here Line segmentation for both printed and handwritten document image is done using two methods namely Histogram projections and Hough Transform assuming that input document image consists of no major skews. For Histogram Projection to work correct, the document must not contain even slight skews. Hough transform gives better results than the former case. Word Segmentation can be done using the connected components analysis. Here, we first identify connected components in the printed or handwritten document image. A methodology is being used here which detects multiple skews in multi handwritten documents or printed ones. Using clustering algorithms, we detect multiple skew blocks in a handwritten document image or printed document image or a combination of both. The algorithm used here also works for skewed multi handwritten text blocks

Segmenting characters from license plate images with little prior knowledge

In this paper, to enable a fast and robust system for automatically recognizing license plates with various appearances, new and simple but efficient algorithms are developed to segment characters from extracted license plate images. Our goal is to segment characters properly from a license plate image region. Different from existing methods for segmenting degraded machine-printed characters, our algorithms are based on very weak assumptions and use no prior knowledge about the format of the plates, in order for them to be applicable to wider applications. Experimental results demonstrate promising efficiency and flexibility of the proposed scheme. © 2010 IEEE

Image Skew Detection and Correction in Regular Images and Document Images

During any Document scanning and processing of regular images in our daily life activities image skew is a very important part that should be kept in mind before processing the images. Skew is generally referred to the degree of rotation of an image in comparison with its actual position. So before proceeding to any further activity with the images we need to assure the skew of an image is correct or not. So detection of skew of an image would be the first thing to be applied to regular images some times and specially scanned documents when transforming them to appropriate format. There are different algorithms for detection of skew of an image that have been implemented in different kind of works. The basic and very commonly used one is Scan line based skew detection. In this technique several lines are passed through the image from left to right, right to left, top to bottom and bottom to top and then the number of black pixels encountered in different projection of line are counted. The projection with maximum black pixels encountered is to be taken to consider the skew of the image. There is another approaches like Hough transform, Base-point method etc. In Hough transform method the pixel value is calculated for each value of θ. The angle producing maximum variance is considered to be the skew angle of the image. These two algorithms have been implemented and the results have been represented to compare the accurac