Document Skew Detection and Correction Algorithm using Wavelet and Radon Transforms

Abstract:As part of research into document image processing, an algorithm has been developed to detect and correct the degree of skew in a scanned image. The principal components of the algorithm are the wavelet and radon transforms. The wavelet transform is used as a form of down-sampling process to reduce the amount of data to be processed. The performance of the algorithm has been evaluated using a sample size of several images taken from different types of document. Evaluation shows that a skew angle may be detected and corrected

Detecção de Inclinação em Imagens de Documentos

A digitalização de documentos contribui para a preservação da informação evitando sua perda devido à degradação física do papel. Atualmente, Sistemas de Reconhecimento Automático de Imagens de Documentos são empregados para converter, automaticamente, a informação contida nas imagens em texto editável, de forma rápida e sem a necessidade da presença de um indivíduo. Assim, tornando essa informação pesquisável através, por exemplo, de palavras-chave.A inclinação em documentos é um problema freqüente nesses sistemas e, em geral, é  imposta durante a digitalização, quando o papel é posicionado com um ângulo diferente de zero grau sobre o eixo do scanner. No caso de documentos manuscritos, a inclinação pode surgir durante a escrita do próprio documento, principalmente quando o escritor não tem uma linha de pauta como guia. A correção da inclinação é essencial para o bom desempenho de sistemas de reconhecimento automático.Este trabalho aborda o problema da detecção de inclinação em documentos impressos e manuscritos, trazendo uma revisão dos principais métodos para detecção de inclinação divulgados na literatura até os dias atuais. As principais técnicas são expostas de forma categorizada e vantagens e limitações de cada método são discutidas

Algorithms for document image skew estimation

A new projection profile based skew estimation algorithm was developed. This algorithm extracts fiducial points representing character elements by decoding a JBIG compressed image without reconstructing the original image. These points are projected along parallel lines into an accumulator array to determine the maximum alignment and the corresponding skew angle. Methods for characterizing the performance of skew estimation techniques were also investigated. In addition to the new skew estimator, three projection based algorithms were implemented and tested using 1,246 single column text zones extracted from a sample of 460 page images. Linear regression analyses of the experimental results indicate that our new skew estimation algorithm performs competitively with the other three techniques. These analyses also show that estimators using connected components as a fiducial representation perform worse than the others on the entire set of text zones. It is shown that all of the algorithms are sensitive to typographical features. The number of text lines in a zone significantly affects the accuracy of the connected component based methods. We also developed two aggregate measures of skew for entire pages. Experiments performed on the 460 unconstrained pages indicate the need to filter non-text features from consideration. Graphic and noise elements from page images contribute a significant amount of the error for the JBIG algorithm

Techniques for document image processing in compressed domain

The main objective for image compression is usually considered the minimization of storage space. However, as the need to frequently access images increases, it is becoming more important for people to process the compressed representation directly. In this work, the techniques that can be applied directly and efficiently to digital information encoded by a given compression algorithm are investigated. Lossless compression schemes and information processing algorithms for binary document images and text data are two closely related areas bridged together by the fast processing of coded data. The compressed domains, which have been addressed in this work, i.e., the ITU fax standards and JBIG standard, are two major schemes used for document compression. Based on ITU Group IV, a modified coding scheme, MG4, which explores the 2-dimensional correlation between scan lines, is developed. From the viewpoints of compression efficiency and processing flexibility of image operations, the MG4 coding principle and its feature-preserving behavior in the compressed domain are investigated and examined. Two popular coding schemes in the area of bi-level image compression, run-length and Group IV, are studied and compared with MG4 in the three aspects of compression complexity, compression ratio, and feasibility of compressed-domain algorithms. In particular, for the operations of connected component extraction, skew detection, and rotation, MG4 shows a significant speed advantage over conventional algorithms. Some useful techniques for processing the JBIG encoded images directly in the compressed domain, or concurrently while they are being decoded, are proposed and generalized; In the second part of this work, the possibility of facilitating image processing in the wavelet transform domain is investigated. The textured images can be distinguished from each other by examining their wavelet transforms. The basic idea is that highly textured regions can be segmented using feature vectors extracted from high frequency bands based on the observation that textured images have large energies in both high and middle frequencies while images in which the grey level varies smoothly are heavily dominated by the low-frequency channels in the wavelet transform domain. As a result, a new method is developed and implemented to detect textures and abnormalities existing in document images by using polynomial wavelets. Segmentation experiments indicate that this approach is superior to other traditional methods in terms of memory space and processing time

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Optical Character Recognition of Printed Persian/Arabic Documents

Texts are an important representation of language. Due to the volume of texts generated and the historical value of some documents, it is imperative to use computers to read generated texts, and make them editable and searchable. This task, however, is not trivial. Recreating human perception capabilities in artificial systems like documents is one of the major goals of pattern recognition research. After decades of research and improvements in computing capabilities, humans\u27 ability to read typed or handwritten text is hardly matched by machine intelligence. Although, classical applications of Optical Character Recognition (OCR) like reading machine-printed addresses in a mail sorting machine is considered solved, more complex scripts or handwritten texts push the limits of the existing technology. Moreover, many of the existing OCR systems are language dependent. Therefore, improvements in OCR technologies have been uneven across different languages. Especially, for Persian, there has been limited research. Despite the need to process many Persian historical documents or use of OCR in variety of applications, few Persian OCR systems work with good recognition rate. Consequently, the task of automatically reading Persian typed documents with close-to-human performance is still an open problem and the main focus of this dissertation. In this dissertation, after a literature survey of the existing technology, we propose new techniques in the two important preprocessing steps in any OCR system: Skew detection and Page segmentation. Then, rather than the usual practice of character segmentation, we propose segmentation of Persian documents into sub-words. The choice of sub-word segmentation is to avoid the challenges of segmenting highly cursive Persian texts to isolated characters. For feature extraction, we will propose a hybrid scheme between three commonly used methods and finally use a nonparametric classification method. A large number of papers and patents advertise recognition rates near 100%. Such claims give the impression that automation problems seem to have been solved. Although OCR is widely used, its accuracy today is still far from a child\u27s reading skills. Failure of some real applications show that performance problems still exist on composite and degraded documents and that there is still room for progress

Advanced document analysis and automatic classification of PDF documents

This thesis explores the domain of document analysis and document classification within the PDF document environment The main focus is the creation of a document classification technique which can identify the logical class of a PDF document and so provide necessary information to document class specific algorithms (such as document understanding techniques). The thesis describes a page decomposition technique which is tailored to render the information contained in an unstructured PDF file into a set of blocks. The new technique is based on published research but contains many modifications which enable it to competently analyse the internal document model of PDF documents. A new level of document processing is presented: advanced document analysis. The aim of advanced document analysis is to extract information from the PDF file which can be used to help identify the logical class of that PDF file. A blackboard framework is used in a process of block labelling in which the blocks created from earlier segmentation techniques are classified into one of eight basic categories. The blackboard's knowledge sources are programmed to find recurring patterns amongst the document's blocks and formulate document-specific heuristics which can be used to tag those blocks. Meaningful document features are found from three information sources: a statistical evaluation of the document's esthetic components; a logical based evaluation of the labelled document blocks and an appearance based evaluation of the labelled document blocks. The features are used to train and test a neural net classification system which identifies the recurring patterns amongst these features for four basic document classes: newspapers; brochures; forms and academic documents. In summary this thesis shows that it is possible to classify a PDF document (which is logically unstructured) into a basic logical document class. This has important ramifications for document processing systems which have traditionally relied upon a priori knowledge of the logical class of the document they are processing

Advanced document analysis and automatic classification of PDF documents

This thesis explores the domain of document analysis and document classification within the PDF document environment The main focus is the creation of a document classification technique which can identify the logical class of a PDF document and so provide necessary information to document class specific algorithms (such as document understanding techniques). The thesis describes a page decomposition technique which is tailored to render the information contained in an unstructured PDF file into a set of blocks. The new technique is based on published research but contains many modifications which enable it to competently analyse the internal document model of PDF documents. A new level of document processing is presented: advanced document analysis. The aim of advanced document analysis is to extract information from the PDF file which can be used to help identify the logical class of that PDF file. A blackboard framework is used in a process of block labelling in which the blocks created from earlier segmentation techniques are classified into one of eight basic categories. The blackboard's knowledge sources are programmed to find recurring patterns amongst the document's blocks and formulate document-specific heuristics which can be used to tag those blocks. Meaningful document features are found from three information sources: a statistical evaluation of the document's esthetic components; a logical based evaluation of the labelled document blocks and an appearance based evaluation of the labelled document blocks. The features are used to train and test a neural net classification system which identifies the recurring patterns amongst these features for four basic document classes: newspapers; brochures; forms and academic documents. In summary this thesis shows that it is possible to classify a PDF document (which is logically unstructured) into a basic logical document class. This has important ramifications for document processing systems which have traditionally relied upon a priori knowledge of the logical class of the document they are processing