Off-line cursive script word recognition

Bozinovic, R.M.; Srihari, Sargur N.

doi:10.1109/34.23114

Cited by 374 publications

(132 citation statements)

References 15 publications

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“…The algorithm used is based on the idea that if a numeral is rotated through a series of slanted positions, it usually attains its minimum width when it is least slanted. Considering " to be the angle that one wants to rotate the digit in the anti-clockwise direction (and -" clockwise), the equation to transform a bit-map by that angle is [8]:…”

Section: Slant Correctionmentioning

confidence: 99%

Reading Courtesy Amounts on Handwritten Paper Checks

2002

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In spite of the rapid advances in computers and communication technologies, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either in an intra-organizational or an inter-organizational environment. One major example of this category of paper intensive applications is the check processing application. In a number of countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. This process of manual reading of each check involves significant time and cost. In this paper, a new approach is proposed to read the numerical amount field on the check; this field is also called the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: the segmentation of the string into a series of individual characters; the normalization of each isolated character; and the recognition of each character based on a neural network classifier.

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Section: Slant Correctionmentioning

confidence: 99%

Reading Courtesy Amounts on Handwritten Paper Checks

2002

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“…Another approach for slant correction was proposed by Guerfali and Plamondon [40] and is based on an offline slant correction method introduced by Bozinovic and Srihari [50].…”

Section: Slant Correction Based On Windowmentioning

confidence: 99%

On Using Entropy for Enhancing Handwriting Preprocessing

Holzinger

Stocker

Peischl³

et al. 2012

Entropy

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Abstract:Handwriting is an important modality for Human-Computer Interaction. For medical professionals, handwriting is (still) the preferred natural method of documentation. Handwriting recognition has long been a primary research area in Computer Science. With the tremendous ubiquity of smartphones, along with the renaissance of the stylus, handwriting recognition has become a new impetus. However, recognition rates are still not 100% perfect, and researchers still are constantly improving handwriting algorithms. In this paper we evaluate the performance of entropy based slant-and skew-correction, and compare the results to other methods. We selected 3700 words of 23 writers out of the Unipen-ICROW-03 benchmark set, which we annotated with their associated error angles by hand. Our results show that the entropy-based slant correction method outperforms a window based approach with an average precision of ±6.02• for the entropy-based method, compared with the ±7.85• for the alternative. On the other hand, the entropy-based skew correction yields a lower average precision of ±2.86• , compared with the average precision of ±2.13• for the alternative LSM based approach.

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“…-Line and word segmentation: a variation of an already existing approach described by Shridar and Kimura [8] is employed since it combines ease of implementation and high accuracy results. -Slant correction: the most commonly used method for slant estimation is the calculation of the average angle of near-vertical strokes [16,17,18]. This approach requires the detection of the edges of the characters and its accuracy strongly depends on the specific characters that compose the word.…”

Section: System Presentationmentioning

confidence: 99%

An unconstrained handwriting recognition system

Kavallieratou¹,

Fakotakis²,

Kokkinakis

2002

International Journal on Document Analysis and Recognition

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Abstract. In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printedhandwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.

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Off-line cursive script word recognition

Cited by 374 publications

References 15 publications

Reading Courtesy Amounts on Handwritten Paper Checks

Reading Courtesy Amounts on Handwritten Paper Checks

On Using Entropy for Enhancing Handwriting Preprocessing

An unconstrained handwriting recognition system

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