scholarly journals Handwritten Kannada Vowels and English Character Recognition System

2012 ◽  
pp. 12-17
Author(s):  
B.V. Dhandra ◽  
Gururaj Mukarambi ◽  
Mallikarjun Hangarge
Keyword(s):  
Character Recognition ◽  
Cross Validation ◽  
Recognition Accuracy ◽  
Recognition System ◽  
English Sample ◽  
X 32 ◽  
Fold Cross Validation

In this paper, a zone based features are extracted from handwritten Kannada Vowels and English uppercase Character images for their recognition. A Total of 4,000 handwritten Kannada and English sample images are collected for classifications. The collected images are normalized into 32 x 32 dimensions. Then the normalized images are divided into 64 zones and their pixel densities are calculated, generating a total of 64 features. These 64 features are submitted to KNN and SVM classifiers with 2 fold cross validation for recognition of the said characters. The proposed algorithm works for individual Kannada vowels, English uppercase alphabets and mixture of both the characters. The recognition accuracy of 92.71% for KNN and 96.00% for SVM classifiers are achieved in case of handwritten Kannada vowels and 97.51% for KNN and 98.26% for SVM classifiers are obtained in case of handwritten English uppercase alphabets. Further, the recognition accuracy of 95.77% and 97.03% is obtained for mixed characters (i.e. Kannada Vowels and English uppercase alphabets). Hence, the proposed algorithm is efficient for the said characters recognition. The proposed algorithm is independent of thinning and slant of the characters and is the novelty of the proposed work.

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

scholarly journals 3D Motion Trajectories Recognition using Optimal Set of Geometric Primitives, Angular and Statistical Features

2019 ◽  
Vol 9 (2) ◽  
pp. 2972-2979
Keyword(s):  
Cross Validation ◽  
Recognition Accuracy ◽  
Svm Classifier ◽  
Statistical Features ◽  
3D Motion ◽  
Motion Trajectories ◽  
Optimal Subset ◽  
Optimal Set ◽  
Geometric Primitives ◽  
Fold Cross Validation

This paper presents the 3D motion trajectories (lower case 3D alphabetic characters) recognition using optimal set of geometric primitives, angular and statistical features. It has been observed that the different combinations of these features have not been used in the literature for recognition of 3D characters. The standard dataset named “CHAR3D” has been used for analysis purpose. The dataset consists of 2858 character samples and each character sample is 3 dimensional pen tip velocity trajectory. In this dataset only single pen down segmented characters have been considered. The recognition has been performed using Random Forest (RF) and multiclass support vector machine (SVM) classifier on the optimal subset of extracted features. The best obtained recognition accuracy of 83.4% has been recorded using 3D points, angular and statistical features at 10 fold cross validation using SVM classifier. Moreover, the highest recognition accuracy of 96.88% has been recorded using an optimal subset of 32 dimensional features namely, geometric primitives, angular and statistical features at 10 fold cross validation by RF classifier.

scholarly journals Automatic Receipt Recognition System Based on Artificial Intelligence Technology

2022 ◽  
Vol 12 (2) ◽  
pp. 853
Author(s):  
Cheng-Jian Lin ◽  
Yu-Cheng Liu ◽  
Chin-Ling Lee
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Character Segmentation ◽  
Small Object ◽  
Labor Costs ◽  
Accuracy Rate ◽  
Artificial Intelligence Technology ◽  
S Model

In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.

Offline Handwritten Gurumukhi Character Recognition System Using Convolution Neural Network

2019 ◽  
Vol 16 (10) ◽  
pp. 4164-4169
Author(s):  
Sheifali Gupta ◽  
Udit Jindal ◽  
Deepali Gupta ◽  
Rupesh Gupta
Keyword(s):  
Character Recognition ◽  
Recognition Accuracy ◽  
Recognition Task ◽  
Recognition System ◽  
Spoken Language ◽  
Processing Method ◽  
Specific Language ◽  
Novel Approach ◽  
The World ◽  
The Right

A lot of literature is available on the recognition of handwriting on scripts other than Indians, but the number of articles related to Indian scripts recognition such as Gurumukhi are much less. Gurumukhi is a religion-specific language that ranks 14th frequently spoken language in all languages of the world. In Gurumukhi script, some characters are alike to each other which makes recognition task very difficult. Therefore this article presents a novel approach for Gurumukhi character. This article lays emphasis on convolutional neural networks (CNN), which intend to obtain the features of given data samples and then its mapping is being performed to the right observation. In this approach, a dataset has been prepared for 10 Gurumukhi characters. The proposed methodology obtains a recognition accuracy of 99.34% on Gurumukhi characters images without making use of any post-processing method.

RECOGNITION OF HANDWRITTEN SIMILAR CHINESE CHARACTERS BY SELF-GROWING PROBABILISTIC DECISION-BASED NEURAL NETWORK

1999 ◽  
Vol 09 (06) ◽  
pp. 545-561 ◽  
Author(s):  
HSIN-CHIA FU ◽  
Y. Y. XU ◽  
H. Y. CHANG
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Difficult Problem ◽  
Input Image ◽  
Prototype System ◽  
Credit Assignment ◽  
Similar Character

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters × 200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance.5,11 As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

scholarly journals Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

2020 ◽  
Author(s):  
Nishatul Majid
Keyword(s):  
Character Recognition ◽  
Singular Solution ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Research Progress ◽  
Writing System ◽  
Flexible Architectures ◽  
Offline Handwriting Recognition ◽  
Alphabetic Writing

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead.

scholarly journals WAVELET DETAIL COEFFICIENT AS A NOVEL WAVELET-MFCC FEATURES IN TEXT-DEPENDENT SPEAKER RECOGNITION SYSTEM

2022 ◽  
Vol 23 (1) ◽  
pp. 68-81
Author(s):  
Syahroni Hidayat ◽  
Muhammad Tajuddin ◽  
Siti Agrippina Alodia Yusuf ◽  
Jihadil Qudsi ◽  
Nenet Natasudian Jaya
Keyword(s):  
Speaker Recognition ◽  
Cross Validation ◽  
Recognition System ◽  
Extraction Methods ◽  
Voice Control ◽  
Testing Data ◽  
Detail Coefficient ◽  
Mel Frequency Cepstral Coefficient ◽  
Critical Process ◽  
Fold Cross Validation

Speaker recognition is the process of recognizing a speaker from his speech. This can be used in many aspects of life, such as taking access remotely to a personal device, securing access to voice control, and doing a forensic investigation. In speaker recognition, extracting features from the speech is the most critical process. The features are used to represent the speech as unique features to distinguish speech samples from one another. In this research, we proposed the use of a combination of Wavelet and Mel Frequency Cepstral Coefficient (MFCC), Wavelet-MFCC, as feature extraction methods, and Hidden Markov Model (HMM) as classification. The speech signal is first extracted using Wavelet into one level of decomposition, then only the sub-band detail coefficient is used as the feature for further extraction using MFCC. The modeled system was applied in 300 speech datasets of 30 speakers uttering “HADIR” in the Indonesian language. K-fold cross-validation is implemented with five folds. As much as 80% of the data were trained for each fold, while the rest was used as testing data. Based on the testing, the system's accuracy using the combination of Wavelet-MFCC obtained is 96.67%. ABSTRAK: Pengecaman penutur adalah proses mengenali penutur dari ucapannya yang dapat digunakan dalam banyak aspek kehidupan, seperti mengambil akses dari jauh ke peranti peribadi, mendapat kawalan ke atas akses suara, dan melakukan penyelidikan forensik. Ciri-ciri khas dari ucapan merupakan proses paling kritikal dalam pengecaman penutur. Ciri-ciri ini digunakan bagi mengenali ciri unik yang terdapat pada sesebuah ucapan dalam membezakan satu sama lain. Penyelidikan ini mencadangkan penggunaan kombinasi Wavelet dan Mel Frekuensi Pekali Cepstral (MFCC), Wavelet-MFCC, sebagai kaedah ekstrak ciri-ciri penutur, dan Model Markov Tersembunyi (HMM) sebagai pengelasan. Isyarat penuturan pada awalnya diekstrak menggunakan Wavelet menjadi satu tahap penguraian, kemudian hanya pekali perincian sub-jalur digunakan bagi pengekstrakan ciri-ciri berikutnya menggunakan MFCC. Model ini diterapkan kepada 300 kumpulan data ucapan daripada 30 penutur yang mengucapkan kata "HADIR" dalam bahasa Indonesia. Pengesahan silang K-lipat dilaksanakan dengan 5 lipatan. Sebanyak 80% data telah dilatih bagi setiap lipatan, sementara selebihnya digunakan sebagai data ujian. Berdasarkan ujian ini, ketepatan sistem yang menggunakan kombinasi Wavelet-MFCC memperolehi 96.67%.

scholarly journals SLOPE AND CURVE CODING TO RECOGNIZE ENGLISH NUMERALS

2020 ◽  
Vol 8 (2) ◽  
pp. 15-20
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
The Novel ◽  
Nearest Neighbour ◽  
Global Database ◽  
Optical Character ◽  
Individual Character ◽  
Nearest Neighbour Classifier

Purpose: Lines and Curves are important parts of characters in any script. Features based on lines and curves go a long way to characterize an individual character as well as differentiate similar-looking characters. The present paper proposes an English numerals recognition system using feature elements obtained from the novel and efficient coding of the curves and local slopes. The purpose of this paper is to recognize English numerals efficiently to develop a reliable Optical Character recognition system. Methodology: K-Nearest Neighbour classification technique has been implemented on a global database MNIST to get an overall recognition accuracy rate of 96.7 %, which is competitive to other reported works in literature. Distance features and slope features are extracted from pre-processed images. The feature elements from training images are used to train K-Nearest-Neighbour classifier and those from test images have been used to classify them. Main Findings: The findings of the current paper can be used in Optical Character Recognition (OCR) of alphanumeric characters of any language, automatic reading of amount on bank cheque, address written on envelops, etc. Implications: Due to the similarity in structures of some numerals like 2, 3, and 8, the system produces respectively lower recognition accuracy rates for them. Novelty: The ways of finding distance and slope features to differentiate the curves in the structure of English Numerals is the novelty of this work.

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