A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.

Multi-experts for touching digit string recognition

84.6 % of touching digit strings have only two digits touching, 12.3 % have three digits touching, and 3.1% have more than three digits touching. We present a multiexperts approach to recognize touching digit pairs (TDP) and touching digit triples (TDT). We combine holistic and traditional segmentation methods. 25,686 TDP training samples and 2778 TDP testing samples collected from USPS mail are used in our experiment. Holistic method outperforms the traditional segmentation based methods. The multi-experts combination has the best performance, a correct rate of 91.1 % on TDP. 1

Recognition of off-line arabic handwritten dates and numeral strings

In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively

An Adaptive modular neural network with application to unconstrained character recognition

"August 1993."Includes bibliographical references (p. 24-27).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Lik Mui ... [et al.

Learning-Based Arabic Word Spotting Using a Hierarchical Classifier

The effective retrieval of information from scanned and written documents is becoming essential with the increasing amounts of digitized documents, and therefore developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Such systems have been implemented for Latin-based and Chinese languages, while few of them have been implemented for Arabic handwriting. The fact that Arabic writing is cursive by nature and unconstrained, with no clear white space between words, makes the processing of Arabic handwritten documents a more challenging problem. In this thesis, the design and implementation of a learning-based Arabic handwritten word spotting system is presented. This incorporates the aspects of text line extraction, handwritten word recognition, partial segmentation of words, word spotting and finally validation of the spotted words. The Arabic text line is more unconstrained than that of other scripts, essentially since it also includes small connected components such as dots and diacritics that are usually located between lines. Thus, a robust method to extract text lines that takes into consideration the challenges in the Arabic handwriting is proposed. The method is evaluated on two Arabic handwritten documents databases, and the results are compared with those of two other methods for text line extraction. The results show that the proposed method is effective, and compares favorably with the other methods. Word spotting is an automatic process to search for words within a document. Applying this process to handwritten Arabic documents is challenging due to the absence of a clear space between handwritten words. To address this problem, an effective learning-based method for Arabic handwritten word spotting is proposed and presented in this thesis. For this process, sub-words or pieces of Arabic words form the basic components of the search process, and a hierarchical classifier is implemented to integrate statistical language models with the segmentation of an Arabic text line into sub-words. The holistic and analytical paradigms (for word recognition and spotting) are studied, and verification models based on combining these two paradigms have been proposed and implemented to refine the outcomes of the analytical classifier that spots words. Finally, a series of evaluation and testing experiments have been conducted to evaluate the effectiveness of the proposed systems, and these show that promising results have been obtained