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Digitization of documents has gained prominence in the recent past for data preserving. Paper documents can be converted to digital form by using various modes of acquisition techniques. In this paper processing of data captured using normal digital camera has been considered. The camera captured document images may contain warped document due to perspective and geometric distortions. Curvature of...
Document layout helps users to focus on important content of the documents while neglecting the rest whenever possible. This paper presents a novel Optical Character Recognition (OCR) algorithm whose performance is enhanced by post-processing based on information collected from document layout analysis. Initial OCR results are used for text block classification, whose results are then used to fine-tune...
Discriminative locality alignment (DLA) has been successfully applied in similar handwritten Chinese character recognition (SHCCR). But, the performance of DLA heavily depends on the choice of parameters and the optimal parameters among different groups of similar characters are not consistent. To address this problem, we present an improved method with few parameters, called adaptive discriminative...
Text-line skew detection and correction is the first step in Arabic document recognition and analysis. It is a crucial pre-processing stage of Arabic Character Recognition (ACR). It has a direct effect on the dependability and efficiency of other system stages such as baseline detection, segmentation and feature extraction stages. In this paper an efficient skew detection and correction method for...
This paper describes a database of on-line handwritten patterns mixed of text, figures, tables, maps, diagrams and so on. Now, pen-based and touch-based interfaces are spreading into people and their surfaces are getting large. People can write and draw mixed objects without paying attention on the difference of objects or the mode change. Moreover, they may write text in any direction in combination...
We study the effect of language orthographic characteristics on the performance of digital word recognition in degraded documents such as historical documents. We provide a rigorous scheme for quantifying the influence of the orthographic characteristics on the quality of word recognition in such documents. We study and compare several orthographic characteristics for four natural languages and measure...
Historical Chinese document recognition technology is important for digital library. However, historical Chinese character segmentation remains a difficult problem due to the complex structure of Chinese characters and various writing styles. This paper presents a novel method for historical Chinese character segmentation based on graph model. After a preliminary over-segmentation stage, the system...
In this paper we propose a neural net based characters recognition scheme for Bangla printed text books. There are a lot of scientific literature, novels, magazines and books etc that are written in Bangla language. More than 400 million people use Bangla language. Most of the library and educational institutions want to keep copy of the books in a digital format. For storing those books in digital...
In this paper, a text line identification method is proposed. The text lines of printed document are easy to segment due to uniform straightness of the lines and sufficient gap between the lines. But in handwritten documents, the line is nonuniform and interline gaps are variable. We take Rabindranath Tagore's manuscript as it is one of the most difficult manuscripts that contain doodles. Our method...
The recognition of handwritten characters is an almost solved problem thanks to efficient machine learning techniques. However, the evaluation and the choice of thresholds to meet a certain level of performance remains a challenge. In this paper, we compare different rejection techniques to determine if a character has been successfully detected or not. Whereas the evaluation of binary classifiers...
In recent years, several methods have been proposed for content-based retrieval from manuscripts, mostly based on character or word similarity. In this paper, we present a new segmentation-free method, called Harris Corner Matching (HCM), which accepts an arbitrary writing pattern as a model and allows to retrieve similar patterns from a possibly large database. Retrieval is performed in two steps...
Presence of multi-oriented characters, connected characters with graphical lines, intersection of text and symbols with graphical lines/curves etc. are very common in graphical documents. As a result word spotting in graphical documents is still a challenging task that we try to solve (partially) in this paper. The proposed approach proceeds in two stages. In the first stage, recognition of isolated...
Certain approaches to writer identification encode handwriting as texture, producing a single histogram of visual features, ignoring any information about the lexical content of the passage. In contrast, other approaches first segment elements of the text, such as characters or big rams, so that they can be compared like-for-like with other instances of the same element. The difference between the...
The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards...
In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm...
In this paper we propose a method of identifying Arabic words from Arabic and Latin scripts in printed documents. This method is based on a statistical and geometric analysis to separate between words of a printed document. Structural features are used to describe the words extracted in previous step. Among the features used: the jambs, the diacritical points, the connected components, the hamps…...
Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both. In this paper, we apply methods recently developed in machine learning -- specifically,...
Document enhancement tools are a valuable help in the study of historic documents. Given proper filter settings, many effects that impair the legibility can be evened out (e.g. washed out ink, stained and yellowed paper). However, because of differing authors, languages, handwritings, fonts and paper conditions, no single filter parameter set fits all documents. Therefore, the parameters are usually...
Recognizing mathematical expressions in PDF documents is a new and important field in document analysis. It is quite different from extracting mathematical expressions in image-based documents. In this paper, we propose a novel method by combining rule-based and learning-based methods to detect both isolated and embedded mathematical expressions in PDF documents. Moreover, various features of formulas,...
More and more fonts have sprung up in recent years in digital publishing industry and reading devices. In this paper, we focus on methods of evaluating digital Chinese fonts and their typeface characteristics to seek a good way to enhance the character recognition rate. To accomplish this, we combined psychological analysis methods with statistical analysis. It involved an extensive survey of distinctive...
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