Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Feature Extraction Methods for Character Recognition

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Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

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%

A. Eye Detection Using Varients of Hough Transform B. Off-Line Signature Verification

PART (A): EYE DETECTION USING VARIANTS OF HOUGH TRANSFORM: Broadly eye detection is the process of tracking the location of human eye in a face image. Previous approaches use complex techniques like neural network, Radial Basis Function networks, Multi-Layer Perceptrons etc. In the developed project human eye is modeled as a circle (iris; the black circular region of eye) enclosed inside an ellipse (eye-lashes). Due to the sudden intensity variations in the iris with respect the inner region of eye-lashes the probability of false acceptance is very less. Since the image taken is a face image the probability of false acceptance further reduces. Hough transform is used for circle (iris) and ellipse (eye-lash) detection. Hough transform was the obvious choice because of its resistance towards the holes in the boundary and noise present in the image. Image smoothing is done to reduce the presence of noise in the image further it makes the image better for further processing like edge detection (Prewitt method). Compared to the aforementioned models the proposed model is simple and efficient. The proposed model can further be improved by including various features like orientation angle of eye-lashes (which is assumed constant in the proposed model), and by making the parameters adaptive. PART (B): OFF-LINE SIGNATURE VERIFICATION: Hand-written signature is widely used for authentication and identification of individual. It has been the target for fraudulence ever since. A novel off-line signature verification algorithm has been developed and tested successfully. Since the hand-written signature can be random, because of presence of various curves and features, techniques like character recognition cannot be applied for signature verification. The proposed algorithm incorporates a soft-computing technique “CLUSTERING” for extraction of feature points from the image of the signature. These feature points or centers are updated using the clustering update equations for required number of times, then these acts as extracted feature points of the signature image. To avoid interpersonal variation 6 to 8 signature images of the same person are taken and feature points are trained. These trained feature points are compared with the test signature images and based on a specific threshold, the signature is declared original or forgery. This approach works well if there is a high variation in the original signature, but for signatures with low variation, it produces incorrect results

A general approach for multi-oriented text line extraction of handwritten document

International audienceThe multi-orientation occurs frequently in ancient handwritten documents, where the writers try to update a document by adding some annotations in the margins. Due to the margin narrowness, this gives rise to lines in different directions and orientations. Document recognition needs to find the lines everywhere they are written whatever their orientation. This is why we propose in this paper a new approach allowing us to extract the multi-oriented lines in scanned documents. Because of the multi-orientation of lines and their dispersion in the page, we use an image meshing allowing us to progressively and locally determine the lines. Once the meshing is established, the orientation is determined using the Wigner-Ville distribution on the projection histogram profile. This local orientation is then enlarged to limit the orientation in the neighborhood. Afterward, the text lines are extracted locally in each zone basing on the follow-up of the orientation lines and the proximity of connected components. Finally, the connected components that overlap and touch in adjacent lines are separated. The morphology analysis of the terminal letters of Arabic words is here considered. The proposed approach has been experimented on 100 documents reaching an accuracy of about 98.6

Segmentation of ancient Arabic documents

International audienceThis chapter addresses the problem of ancient Arabic document segmentation. As ancient documents neither have a real physical structure nor logical one, the segmentation will be limited to textual area or to line extraction in the areas. Although this type of segmentation appears quite simple, its implementation remains a challenging task. This is due to the state of the old document where the image is of low quality, the lines are not straight, sinuous and connected. Given the failure of traditional methods, we proposed a method for line extraction in multi-oriented documents. The method is based on an image meshing that allows it to detect locally and safely the orientations. These orientations are then extended to larger areas. The orientation estimation uses the energy distribution of Cohen's class, more accurate than the projection method. Then, the method exploits the projection peaks to follow the connected components forming text lines. The approach ends with a final separation of connected lines, based on the exploitation of the morphology of terminal letters

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 data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field