Neural network-based systems for handprint OCR applications

Ganis, M.D.; Wilson, Charles L.; Blue, James L.

doi:10.1109/83.704304

Cited by 50 publications

(28 citation statements)

References 47 publications

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“…Healthcare [2] has also seen an increase in the use of OCR technology to process paperwork. Healthcare professionals always have to deal with large volumes of forms for each patient, including insurance forms as well as general health forms.…”

Section: Healthcarementioning

confidence: 99%

“…Legal industry [2] is also one of the beneficiaries of the OCR technology. OCR is used to digitize documents, and directly entered to computer database.…”

Section: B Legal Industrymentioning

confidence: 99%

“…Another important application of OCR is in banking [2], where it is used to process cheques without human involvement. A cheque can be inserted into a machine where the system scans the amount to be issued and the correct amount of money is transferred.…”

Section: Bankingmentioning

confidence: 99%

“…Invoice imaging [2] is widely used in many businesses applications to keep track of financial records and prevent a backlog of payments from piling up. In government agencies and independent organizations, OCR simplifies data collection and analysis, among other processes.…”

Section: A Invoice Imagingmentioning

confidence: 99%

“…Optical Character Recognition is a vast field with a number of varied applications such as invoice imaging [2], legal industry [2], banking, health care industry [2] etc. OCR is also widely used in many other fields like Captcha [3], Institutional repositories and digital libraries [4], Optical Music Recognition [5] without any human correction or human effort, Automatic number plate recognition [6] and Handwritten Recognition [7] .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of OCR Applications

Singh¹,

Bacchuwar²,

Bhasin³

2012

IJMLC

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Abstract-Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and search text from electronic documents or to publish the text on a website. The paper presents a survey of applications of OCR in different fields and further presents the experimentation for three important applications such as Captcha, Institutional Repository and Optical Music Character Recognition. We make use of an enhanced image segmentation algorithm based on histogram equalization using genetic algorithms for optical character recognition. The paper will act as a good literature survey for researchers starting to work in the field of optical character recognition.

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Section: Healthcarementioning

confidence: 99%

“…Legal industry [2] is also one of the beneficiaries of the OCR technology. OCR is used to digitize documents, and directly entered to computer database.…”

Section: B Legal Industrymentioning

confidence: 99%

Section: Bankingmentioning

confidence: 99%

Section: A Invoice Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of OCR Applications

Singh¹,

Bacchuwar²,

Bhasin³

2012

IJMLC

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Text Recognition for 2D Bridge Plans Using OCR‐Algorithms

Peng,

Kang,

Marx

2023

ce papers

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The 2D bridge plans play an essential role in renovations or new replacements of transport infrastructures. As an indispensable component of plans, text serves as dimension labels, descriptions of materials, etc. Optical Character Recognition (OCR) can convert text in image into machine‐encoded format, and has high potential and invaluable practical worth for analyzing, managing, and extracting textual information from plans. First, the selection of OCR‐algorithms and the sampling of textual fragments from 2D bridge plans, including historic plans with handwritten text and digital plans with printed text, was carried out. Subsequently, the performances of different open‐source OCR‐algorithms on different textual fragments from 2D bridge plans are compared. In this process, the accuracy at character level and word level are chosen as two main factors to evaluate their performances. Finally, the most potential OCR‐algorithms for the text recognition on 2D bridge plans are proposed.

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Recognition of Handprinted Bangla Numerals Using Neural Network Models

Bhattacharya

Das

Datta

et al. 2002

Advances in Soft Computing — AFSS 2002

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Abstract. This paper proposes an automatic recognition scheme for handprinted Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.

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Neural network-based systems for handprint OCR applications

Cited by 50 publications

References 47 publications

A Survey of OCR Applications

A Survey of OCR Applications

Text Recognition for 2D Bridge Plans Using OCR‐Algorithms

Recognition of Handprinted Bangla Numerals Using Neural Network Models

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