Automatic detection of change in address blocks for reply forms processing

In this paper, an automatic method to detect the presence of on-line erasures/scribbles/corrections/over-writing in the address block of various types of subscription and utility payment forms is presented. The proposed approach employs bottom-up segmentation of the address block. Heuristic rules based on structural features are used to automate the detection process. The algorithm is applied on a large dataset of 5,780 real world document forms of 200 dots per inch resolution. The proposed algorithm performs well with an average processing time of 108 milliseconds per document with a detection accuracy of 98.96%

Segmentation of Unstructured Newspaper Documents

Document layout analysis is one of the important steps in automated document recognition systems. In Document layout analysis, meaningful information is retrieved from document images by identifying, categorizing and labeling the semantics of text blocks from the document images. In this paper, we present simple top-down approach for document page segmentation. We have tested the proposed method on unstructured documents like newspaper which is having complex structures having no fixed structure. Newspaper also has multiple titles and multiple columns. In the proposed method, white gap area which separates titles, columns of text, line of text and words in lines have been identified to separate document into various segments. The proposed algorithm has been successfully implemented and applied over a large number of Indian newspapers and the results have been evaluated by number of blocks detected and taking their correct ordering information into account

Character Type Classification via Probabilistic Topic Model

ArticleInternational Journal of Signal Processing, Image Processing and Pattern Recognition. 5(2): 123-140 (2012)journal articl

RECOGNITION OF PRINTED KANNADA NUMERALS BY NEAREST NEIGHBOR METHOD

Numeral recognition is considered to be very prominent in most of the Character recognition researches. With respect to applications like number plate recognition and document processing the numerals are composed as a part of number plate images/application form type document images. This paper mainly focuses on eliminating language barriers that may arise while comprehending the regional language numerals by a non-regional user at the time of number plate recognition or other application form type document processing with special reference to Karnataka state. An algorithm is devised by incorporating the capabilities of functionalities of features the handwritten and printed Kannada numerals

Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms With an Inference Engine for Effective Clinical Diagnosis and Treatment

Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) makes CDSS an information island, i.e., additional input work on patient information might be required, which would further increase the burden on clinicians. One popular strategy is the integration of CDSS in HIS to directly read electronic health records (EHRs) for analysis. However, gathering data from EHRs could constitute another problem, because EHR document standards are not unified. In addition, HIS could use different default clinical terminologies to define input data, which could cause additional misinterpretation. Several proposals have been published thus far to allow CDSS access to EHRs via the redefinition of data terminologies according to the standards used by the recipients of the data flow, but they mostly aim at specific versions of CDSS guidelines. This paper views these problems in a different way. Compared with conventional approaches, we suggest more fundamental changes; specifically, uniform and updatable clinical terminology and document syntax should be used by EHRs, HIS, and their integrated CDSS. Facilitated data exchange will increase the overall data loading efficacy, enabling CDSS to read more information for analysis at a given time. Furthermore, a proposed CDSS should be based on self-learning, which dynamically updates a knowledge model according to the data-stream-based upcoming data set. The experiment results show that our system increases the accuracy of the diagnosis and treatment strategy designs

A Hierarchical Cluster Tree Approach Leveraging Delaunay Triangulation

This research introduces a robust and reliable technique for structuring document image pages hierarchically, harnessing the power of Delaunay triangulation. Central to our approach is the formation of a cluster tree, which encapsulates the page's content through strategically exploiting layout elements arrangements and their relative distances. By applying our technique, we proficiently categorize the page into distinct clusters encompassing images, titles, and paragraphs. The consequent hierarchical framework, founded on the cluster tree, establishes a durable and trustworthy blueprint of the document layout, thereby accelerating document comprehension and examination.</p

Page layout analysis and classification in complex scanned documents

Page layout analysis has been extensively studied since the 1980`s, particularly after computers began to be used for document storage or database units. For efficient document storage and retrieval from a database, a paper document would be transformed into its electronic version. Algorithms and methodologies are used for document image analysis in order to segment a scanned document into different regions such as text, image or line regions. To contribute a novel approach in the field of page layout analysis and classification, this algorithm is developed for both RGB space and grey-scale scanned documents without requiring any specific document types, and scanning techniques. In this thesis, a page classification algorithm is proposed which mainly applies wavelet transform, Markov random field (MRF) and Hough transform to segment text, photo and strong edge/ line regions in both color and gray-scale scanned documents. The algorithm is developed to handle both simple and complex page layout structures and contents (text only vs. book cover that includes text, lines and/or photos). The methodology consists of five modules. In the first module, called pre-processing, image enhancements techniques such as image scaling, filtering, color space conversion or gamma correction are applied in order to reduce computation time and enhance the scanned document. The techniques, used to perform the classification, are employed on the one-fourth resolution input image in the CIEL*a*b* color space. In the second module, the text detection module uses wavelet analysis to generate a text-region candidate map which is enhanced by applying a Run Length Encoding (RLE) technique for verification purposes. The third module, photo detection, initially uses block-wise segmentation which is based on basis vector projection technique. Then, MRF with maximum a-posteriori (MAP) optimization framework is utilized to generate photo map. Next, Hough transform is applied to locate lines in the fourth module. Techniques for edge detection, edge linkages, and line-segment fitting are used to detect strong-edges in the module as well. After those three classification maps are obtained, in the last module a final page layout map is generated by using K-Means. Features are extracted to classify the intersection regions and merge into one classification map with K-Means clustering. The proposed technique is tested on several hundred images and its performance is validated by utilizing Confusion Matrix (CM). It shows that the technique achieves an average of 85% classification accuracy rate in text, photo, and background regions on a variety of scanned documents like articles, magazines, business-cards, dictionaries or newsletters etc. More importantly, it performs independently from a scanning process and an input scanned document (RGB or gray-scale) with comparable classification quality

Automatic document classification and extraction system (ADoCES)

Document processing is a critical element of office automation. Document image processing begins from the Optical Character Recognition (OCR) phase with complex processing for document classification and extraction. Document classification is a process that classifies an incoming document into a particular predefined document type. Document extraction is a process that extracts information pertinent to the users from the content of a document and assigns the information as the values of the “logical structure” of the document type. Therefore, after document classification and extraction, a paper document will be represented in its digital form instead of its original image file format, which is called a frame instance. A frame instance is an operable and efficient form that can be processed and manipulated during document filing and retrieval. This dissertation describes a system to support a complete procedure, which begins with the scanning of the paper document into the system and ends with the output of an effective digital form of the original document. This is a general-purpose system with “learning” ability and, therefore, it can be adapted easily to many application domains. In this dissertation, the “logical closeness” segmentation method is proposed. A novel representation of document layout structure - Labeled Directed Weighted Graph (LDWG) and a methodology of transforming document segmentation into LDWG representation are described. To find a match between two LDWGs, string representation matching is applied first instead of doing graph comparison directly, which reduces the time necessary to make the comparison. Applying artificial intelligence, the system is able to learn from experiences and build samples of LDWGs to represent each document type. In addition, the concept of frame templates is used for the document logical structure representation. The concept of Document Type Hierarchy (DTH) is also enhanced to express the hierarchical relation over the logical structures existing among the documents