scholarly journals Script Identification for Devanagari and Gurumukhi using OCR

2021 ◽  
Vol 10 (9) ◽  
pp. 12-22
Author(s):  
Shubhankar Sharma ◽  
Vatsala Arora
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Images ◽  
Script Identification ◽  
Pattern Recognition Techniques ◽  
Recognition Process ◽  
Optical Character ◽  
Handwritten Text ◽  
Handwritten Document

The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

Handwritten Indic Script Identification in Multi-Script Document Images: A Survey

2018 ◽  
Vol 32 (10) ◽  
pp. 1856012 ◽  
Author(s):  
Sk. Md. Obaidullah ◽  
K. C. Santosh ◽  
Nibaran Das ◽  
Chayan Halder ◽  
Kaushik Roy
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Images ◽  
Extraction Techniques ◽  
Script Identification ◽  
Optical Character ◽  
Comprehensive Survey ◽  
Indic Script

Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challenges.

Intriguing Aspects of Oriental Languages

1998 ◽  
Vol 12 (01) ◽  
pp. 5-29 ◽  
Author(s):  
Ching Y. Suen ◽  
Shunji Mori ◽  
Hae-Chang Rim ◽  
Patrick S. P. Wang
Keyword(s):  
Pattern Recognition ◽  
Computational Linguistics ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Contextual Information ◽  
Future Trends ◽  
Pattern Recognition Techniques ◽  
Optical Character

This paper includes a description of 3 affiliated oriental languages: Chinese, Japanese, and Korean. It includes a description of the origins of these 3 languages and the inter-relationship among them. Drawn from the viewpoints of several experienced researchers in the field of OCR (Optical Character Recognition) and computational linguistics, it attempts to bring out the intriguing aspects of these 3 ideographic languages, including the formation and composition of pictograms, special features, learning, understanding, contextual information, and recognition of characters and words, and their relations to poetic expressions and pattern recognition techniques. Numerous references are given and comments on future trends are also presented.

Multi-Oriented Text Extraction in Stylistic Documents

2015 ◽  
Vol 15 (01) ◽  
pp. 1550002
Author(s):  
Brij Mohan Singh ◽  
Rahul Sharma ◽  
Debashis Ghosh ◽  
Ankush Mittal
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Morphological Operations ◽  
Document Images ◽  
Font Size ◽  
Text Extraction ◽  
Optical Character ◽  
Engineering Drawings ◽  
Flood Fill

In many documents such as maps, engineering drawings and artistic documents, etc. there exist many printed as well as handwritten materials where text regions and text-lines are not parallel to each other, curved in nature, and having various types of text such as different font size, text and non-text areas lying close to each other and non-straight, skewed and warped text-lines. Optical character recognition (OCR) systems available commercially such as ABYY fine reader and Free OCR, are not capable of handling different ranges of stylistic document images containing curved, multi-oriented, and stylish font text-lines. Extraction of individual text-lines and words from these documents is generally not straight forward. Most of the segmentation works reported is on simple documents but still it remains a highly challenging task to implement an OCR that works under all possible conditions and gives highly accurate results, especially in the case of stylistic documents. This paper presents dilation and flood fill morphological operations based approach that extracts multi-oriented text-lines and words from the complex layout or stylistic document images in the subsequent stages. The segmentation results obtained from our method proves to be superior over the standard profiling-based method.

A WAVE APPROACH TO PATTERN RECOGNITION (WITH APPLICATION TO OPTICAL CHARACTER RECOGNITION)

1994 ◽  
Vol 04 (01) ◽  
pp. 193-207 ◽  
Author(s):  
VADIM BIKTASHEV ◽  
VALENTIN KRINSKY ◽  
HERMANN HAKEN
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Dissipative Structures ◽  
Binary Images ◽  
Optical Character ◽  
Nonlinear Version ◽  
Medium Quality ◽  
Scalar Products ◽  
Machine Reading

The possibility of using nonlinear media as a highly parallel computation tool is discussed, specifically for image classification and recognition. Some approaches of this type are known, that are based on stationary dissipative structures which can “measure” scalar products of images. In this paper, we exploit the analogy between binary images and point sets, and use the Hausdorff metrics for comparing the images. It does not require the measure at all, and is based only on the metrics of the space whose subsets we consider. In addition to Hausdorff distance, we suggest a new “nonlinear” version of this distance for comparison of images, called “autowave” distance. This distance can be calculated very easily and yields some additional advantages for pattern recognition (e.g. noise tolerance). The method was illustrated for the problem of machine reading (Optical Character Recognition). It was compared with some famous OCR programs for PC. On a medium quality xerocopy of a journal page, in the same conditions of learning and recognition, the autowave approach resulted in much fewer mistakes. The method can be realized using only one chip with simple uniform connection of the elements. In this case, it yields an increase in computation speed of several orders of magnitude.

Word-Level Script Identification Using Texture Based Features

2015 ◽  
Vol 4 (2) ◽  
pp. 74-94
Author(s):  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Statistical Significance ◽  
Document Image ◽  
Statistical Significance Testing ◽  
Script Identification ◽  
Word Level ◽  
Histograms Of Oriented Gradients ◽  
Handwritten Text ◽  
Identification Technique

Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

scholarly journals Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features

2021 ◽  
Vol 9 (VI) ◽  
pp. 1755-1758
Author(s):  
Rohan Modi
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Handwriting Recognition ◽  
Cost Effective ◽  
Computer Hardware ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Audio Output

Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.

scholarly journals Corpus-based technique for improving Arabic OCR system

2021 ◽  
Vol 21 (1) ◽  
pp. 233
Author(s):  
Ahmed Hussain Aliwy ◽  
Basheer Al-Sadawi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Language Model ◽  
Arabic Language ◽  
Document Images ◽  
Statistical Language Model ◽  
Text Document ◽  
Optical Character ◽  
Arabic Ocr

<p><span>An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. </span></p>

scholarly journals Optical Character Recognition from Printed Text Images

2019 ◽  
pp. 597-604 ◽  
Author(s):  
Dr. T. Kameswara Rao ◽  
K. Yashwanth Chowdary ◽  
I. Koushik Chowdary ◽  
K. Prasanna Kumar ◽  
Ch. Ramesh
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Corner Point ◽  
Document Retrieval ◽  
Document Images ◽  
Image Region ◽  
Text Documents ◽  
Optical Character ◽  
Simple Program ◽  
Point Detection

In recent years, text extraction from document images is one of the most widely studied topics in Image Analysis and Optical Character Recognition. These extractions of document images can be used for document analysis, content analysis, document retrieval and many more. Many complex text extracting processes Maximization Likelihood (ML), Edge point detection, Corner point detection etc. are used to extract text documents from images. In this article, the corner point approach was used. To extract document from images we used a very simple approach based on FAST algorithm. Firstly, we divided the image into blocks and their density in each block was checked. The denser blocks were labeled as text blocks and the less dense were the image region or noise. Then we check the connectivity of the blocks to group the blocks so that the text part can be isolated from the image. This method is very fast and versatile, it can be used to detect various languages, handwriting and even images with a lot of noise and blur. Even though it is a very simple program the precision of this method is closer or higher than 90%. In conclusion, this method helps in more accurate and less complex detection of text from document images.

scholarly journals Character Segmentation and Skew Correction for Handwritten Devanagari Scripts: A Friends Technique

2019 ◽  
Vol 8 (1) ◽  
pp. 50-54
Author(s):  
Ashok Kumar Bathla . ◽  
Sunil Kumar Gupta .
Keyword(s):  
Human Brain ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Novel Technique ◽  
Skew Correction ◽  
Optical Character ◽  
Handwritten Text ◽  
Scripting Language ◽  
The Way

Optical Character Recognition (OCR) technology allows a computer to “read” text (both typed and handwritten) the way a human brain does.Significant research efforts have been put in the area of Optical Character Segmentation (OCR) of typewritten text in various languages, however very few efforts have been put on the segmentation and skew correction of handwritten text written in Devanagari which is a scripting language of Hindi. This paper aims a novel technique for segmentation and skew correction of hand written Devanagari text. It shows the accuracy of 91% and takes less than one second to segment a particular handwritten word.

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