A simple and efficient optical character recognition system for basic symbols in printed Kannada text

Kunte, R. Sanjeev; Samuel, R. D. Sudhaker

doi:10.1007/s12046-007-0039-1

Cited by 47 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, there are no large-scale public datasets for the problem of printed text recognition of Indian languages. Most of the early works in this space use a dataset of cropped characters or isolated symbols since these works deal with classification of disjoint characters [10,57]. Later works that make use of line or word level annotated data use either internal collections [13,31,33,40,59] or large-scale synthetically generated samples [4,16,35] to train their models.…”

Section: Mozhi Datasetmentioning

confidence: 99%

An empirical study of CTC based models for OCR of Indian languages

Mathew¹,

Jawahar²

2022

Preprint

View full text Add to dashboard Cite

Recognition of text on word or line images, without the need for sub-word segmentation has become the mainstream of research and development of text recognition for Indian languages. Modelling unsegmented sequences using Connectionist Temporal Classification (CTC) is the most commonly used approach for segmentation-free OCR. In this work we present a comprehensive empirical study of various neural network models that uses CTC for transcribing step-wise predictions in the neural network output to a Unicode sequence. The study is conducted for 13 Indian languages, using an internal dataset that has around 1000 pages per language. We study the choice of line vs word as the recognition unit, and use of synthetic data to train the models. We compare our models with popular publicly available OCR tools for end-to-end document image recognition. Our end-to-end pipeline that employ our recognition models and existing text segmentation tools outperform these public OCR tools for 8 out of the 13 languages. We also introduce a new public dataset called Mozhi for word and line recognition in Indian language. The dataset contains more than 1.2 million annotated word images (120 thousand text lines) across 13 Indian languages. Our code, trained models and the Mozhi dataset will be made available at cvit-projects

show abstract

Section: Mozhi Datasetmentioning

confidence: 99%

An empirical study of CTC based models for OCR of Indian languages

Mathew¹,

Jawahar²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A simple and efficient optical character recognition system for basic symbols in printed Kannada text [28] Kannada characters Seven Moments + RBF Neural Networks 82% Offline Handwriting Recognition using Genetic Algorithm [29] English characters Neural Networks + Genetic algorithms 71%.…”

Section: Character Used Main Features Percentagementioning

confidence: 99%

Neural Network with Madaline for Machine Printed English Character Recognition

Al-mashahadany¹

2011

AL-Rafidain Journal of Computer Sciences and Mathematics

View full text Add to dashboard Cite

The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence.In this research the application of neural networks to the problem of identifying English machine printed characters in an automated manner is developed. A preprocessing step is implemented to separate each character from the others. After that a feature extraction process is applied on each character to obtain the minimum nodes by using Mean, Standard Deviation, and Variance. Madaline neural network is trained on a 26 alphabetical English characters with a standard font and size. And tested on these characters to verify each character image belongs to which type of character. This is done by using MATLAB®2008a.

show abstract

“…for (p+q) = 2,3,… A set of seven moment invariants can be derived from equations (6) While processing, each image is resized into 40X40 pixel image. The image obtained represents the number with black color on a white background.…”

Section: Moment Invariants (Mis)mentioning

confidence: 99%

“…However, Hu's moments are not derived from family of orthogonal functions, and so contain much redundant information about a character's shape. Hence, Zernike moments based on the theory of orthogonal polynomials are becoming popular for character recognition nowadays [6].Since handwritten characters are inclusive of variations in style fonts etc. .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Recognition of Handwritten Arabic Numerals

ALI¹,

RJ²

2011

Int J Mach Intell

View full text Add to dashboard Cite

Human generated patterns like handwritten characters are found to be fuzzy in nature upto certain extent. This presented work proposes a fuzzy conceptual approach to classify Handwritten Arabic Numerals based on invariant moments features and the divisions of numeral image into several parts. The Moment invariants features are well known for independence of size, slant, orientation, translation and other variations of handwritten characters. A database, created by American University in Cairo, of 7000 samples of each number from 700 different writers is used. Each image is normalized to 40X40 pixel size. Seven central invariant moments are evaluated for each image and its parts by dividing it by three different ways, i.e. three feature groups. The algorithm is experimented for 500 samples of each numeral image and 161 features were evaluated corresponding to each image. The performance rate of the method is found to be 95.14%.

show abstract

A simple and efficient optical character recognition system for basic symbols in printed Kannada text

Cited by 47 publications

References 14 publications

An empirical study of CTC based models for OCR of Indian languages

An empirical study of CTC based models for OCR of Indian languages

Neural Network with Madaline for Machine Printed English Character Recognition

Fuzzy Based Recognition of Handwritten Arabic Numerals

Contact Info

Product

Resources

About