Multi-font Numerals Recognition for Urdu Script based Languages

International audienceHandwritten character recognition of Urdu script based languages is one of the most difficult task due to complexities of the script. Urdu script based languages has not received much attestation even this script is used more than 1/6th of the population. The complexities in the script makes more complicated the recognition process. The problem in handwritten numeral recognition is the shape similarity between handwritten numerals and dual style for Urdu. This paper presents a fuzzy rule base, HMM and Hybrid approaches for the recognition of numerals both Urdu and Arabic in unconstrained environment from both online and offline domain for online input. Basically offline domain is used for preprocessing i.e normalization, slant normalization. The proposed system is tested and provides accuracy of 97.1

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

Feature Extraction Methods for Character Recognition

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A novel image matching approach for word spotting

Word spotting has been adopted and used by various researchers as a complementary technique to Optical Character Recognition for document analysis and retrieval. The various applications of word spotting include document indexing, image retrieval and information filtering. The important factors in word spotting techniques are pre-processing, selection and extraction of proper features and image matching algorithms. The Correlation Similarity Measure (CORR) algorithm is considered to be a faster matching algorithm, originally defined for finding similarities between binary patterns. In the word spotting literature the CORR algorithm has been used successfully to compare the GSC binary features extracted from binary word images, i.e., Gradient, Structural and Concavity (GSC) features. However, the problem with this approach is that binarization of images leads to a loss of very useful information. Furthermore, before extracting GSC binary features the word images must be skew corrected and slant normalized, which is not only difficult but in some cases impossible in Arabic and modified Arabic scripts. We present a new approach in which the Correlation Similarity Measure (CORR) algorithm has been used innovatively to compare Gray-scale word images. In this approach, binarization of images, skew correction and slant normalization of word images are not required at all. The various features, i.e., projection profiles, word profiles and transitional features are extracted from the Gray-scale word images and converted into their binary equivalents, which are compared via CORR algorithm with greater speed and higher accuracy. The experiments have been conducted on Gray-scale versions of newly created handwritten databases of Pashto and Dari languages, written in modified Arabic scripts. For each of these languages we have used 4599 words relating to 21 different word classes collected from 219 writers. The average precision rates achieved for Pashto and Dari languages were 93.18 % and 93.75 %, respectively. The time taken for matching a pair of images was 1.43 milli-seconds. In addition, we will present the handwritten databases for two well-known Indo- Iranian languages, i.e., Pashto and Dari languages. These are large databases which contain six types of data, i.e., Dates, Isolated Digits, Numeral Strings, Isolated Characters, Different Words and Special Symbols, written by native speakers of the corresponding languages

The rectification and recognition of document images with perspective and geometric distortions

Ph.DDOCTOR OF PHILOSOPH

Two-dimensional penalized signal regression for hand written digit recognition

Many attempts have been made to achieve successful recognition of handwritten digits. We report our results of using statistical method on handwritten digit recognition. A digitized handwritten numeral can be represented by an image with grayscales. The image includes features that are mapped into two-dimensional space with row and column coordinates. Based on this structure, two-dimensional penalized signal logistic regression (PSR) is applied to the recognition of handwritten digits. The data set is taken from the USPS zip code database that contains 7219 training images and 2007 test images. All the images have been deslanted and normalized into 16 x 16 pixels with various grayscales. The PSR method constructs a coefficient surface using a rich two-dimensional tensor product B-splines basis, so that the surface is more flexible than needed. We then penalize roughness of the coefficient surface with difference penalties on each coefficient associate with the rows and columns of the tensor product B-splines. The optimal penalty weight is found in several minutes of iterative operations. A competitive overall recognition error rate of 8.97% on the test data set was achieved. We will also review an artificial neural network approach for comparison. By using PSR, it requires neither long learning time nor large memory resources. Another advantage of the PSR method is that our results are obtained on the original USPS data set without any further image preprocessing. We also found that PSR algorithm was very capable to cope with high diversity and variation that were two major features of handwritten digits

Automation of Indian Postal Documents written in Bangla and English

International audienceIn this paper, we present a system towards Indian postal automation based on pin-code and city name recognition. Here, at first, using Run Length Smoothing Approach (RLSA), non-text blocks (postal stamp, postal seal, etc.) are detected and using positional information Destination Address Block (DAB) is identified from postal documents. Next, lines and words of the DAB are segmented. In India, the address part of a postal document may be written by combination of two scripts: Latin (English) and a local (State/region) script. It is very difficult to identify the script by which pin-code part is written. To overcome this problem on pin-code part, we have used two-stage artificial neural network based general scheme to recognize pin-code numbers written in any of the two scripts. To identify the script by which a word/city name is written, we propose a water reservoir concept based feature. For recognition of city names, we propose an NSHP-HMM (Non- Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the proposed digit numeral recognition module is 93.14% while that of city name recognition scheme is 86.44%

Novel word recognition and word spotting systems for offline Urdu handwriting

Word recognition for offline Arabic, Farsi and Urdu handwriting is a subject which has attained much attention in the OCR field. This thesis presents the implementations of offline Urdu Handwritten Word Recognition (HWR) and an Urdu word spotting technique. This thesis first introduces the creation of several offline CENPARMI Urdu databases. These databases were necessary for offline Urdu HWR experiments. The holistic-based recognition approach was followed for the Urdu HWR system. In this system, the basic pre-processing of images was performed. In the feature extraction phase, the gradient and structural features were extracted from greyscale and binary word images, respectively. This recognition system extracted 592 feature sets and these features helped in improving the recognition results. The system was trained and tested on 57 words. Overall, we achieved a 97 % accuracy rate for handwritten word recognition by using the SVM classifier. Our word spotting technique used the holistic HWR system for recognition purposes. This word spotting system consisted of two processes: the segmentation of handwritten connected components and diacritics from Urdu text lines and the word spotting algorithm. A small database of handwritten text pages was created for testing the word spotting system. This database consisted of texts from ten Urdu native speakers. The rule-based segmentation system was applied for segmentation (or extracting) for handwritten Urdu subwords or connected components from text lines. We achieved a 92% correct segmentation rate for 372 text lines. In the word spotting algorithm, the candidate words were generated from the segmented connected components. These candidate words were sent to the holistic HWR system, which extracted the features and tried to recognize each image as one of the 57 words. After classification, each image was sent to the verification/rejection phase, which helped in rejecting the maximum number of unseen (raw data) images. Overall, we achieved a 50% word spotting precision at a 70% recall rat

Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts