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.

scholarly journals Pengenalan Karakter Plat Nomor Mobil Secara Real Time

2013 ◽  
Vol 7 (1) ◽  
pp. 35
Author(s):  
Ikhwan Ruslianto ◽  
Agus Harjoko
Keyword(s):  
Success Rate ◽  
Real Time ◽  
Character Recognition ◽  
Template Matching ◽  
Component Analysis ◽  
Recognition System ◽  
Character Segmentation ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Complex Background

AbstrakPengenalan plat nomor di Indonesia biasanya digunakan pada sistem parkir yang masih dilakukan secara manual, yaitu dengan mencatat karakter plat nomor oleh petugas jaga parkir. Padahal pengenalan plat nomor tidak hanya dilakukan untuk system perparkiran tetapi dapat digunakan untuk menemukan kendaraan yang melanggar peraturan lalu lintas dijalan raya secara real time, misalnya pelaku tabrak lari pada kecelakaan maupun kendaraan yang melanggar rambu-rambu lalu lintas.Penelitian ini memberikan alternatif pengenalan karakter plat nomor mobil menggunakan metode connected component analysis dan matching sehingga dapat menyelesaikan permasalahan dengan background yang kompleks dan mobil yang bergerak dijalan raya.Metode connected component analysis berhasil melakukan proses segmentasi plat dan segmentasi karakter dengan kondisi background yang kompleks secara tepat terhadap 67 sampel citra dengan tingkat keberhasilan 95,52% untuk segmentasi plat dan 94,98% untuk segmentasi karakter dan metode template matching berhasil melakukan proses pengenalan karakter secara akurat dengan tingkat keberhasilan 87,45%. Kata kunci— real time, connected component analysis, template matching  Abstract Indonesia’s number plat recognition system are typically used in parking lots that are still done manually, by recording the license plate characters by parking guard. Though number plate recognition system is not only for parking but can be used to find vehicles that violate traffic rules highway street in real time, such as actors on the hit and run accident and the vehicles that violate traffic signs.This study provides an alternative car number plate character recognition using connected component analysis and matching so as to solve problems with complex background and a moving car on the road.Connected component analysis method successfully to the plates segmentation and character segmentation in complex background condition are appropriate to the 67 sample images with the success rate of 95.52% for the plate segmentation and 94.98% for plate character segmentation and template matching method successfully perform the character recognition process accurately with a success rate of 87.45%. Keywords— real time, connected component analysis, template matching

scholarly journals Program Konversi Citra Notasi Balok Menjadi Notasi Angka

2018 ◽  
Vol 2 (2) ◽  
pp. 119
Author(s):  
Hendy Gunawan ◽  
Janson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Success Rate ◽  
Character Recognition ◽  
Template Matching ◽  
Recognition System ◽  
Input Image ◽  
Character Segmentation ◽  
Music Notation ◽  
Image Conversion ◽  
Template Image ◽  
Line Segmentation

The Image Conversion Program of Music Notation being Numeric Notation is a character recognition system that accepts input in form of music notation image that produces an output of a DOCX file containing the numeric notation from the input image. Music notation has notation value, ritmic value and written with a music stave. The system consists of four main processes: preprocessing (grayscale and thresholding), notation line segmentation, notation character segmentation, and template matching. Template matching is used to recognize the music notation that obtained after segmentation. The recognition process obtained by comparing the image with the template image that has been inputted before to the database. This system has 100% success rate on segmentation of the character and success rate 38,4843% on the character recognition with template matching.

Research and Design of License Plate Recognition System

2014 ◽  
Vol 556-562 ◽  
pp. 2623-2627
Author(s):  
Feng Ran ◽  
Fa Yu Zhang ◽  
Mei Hua Xu
Keyword(s):  
Adaptive Learning ◽  
Character Recognition ◽  
Complete System ◽  
Recognition System ◽  
Morphological Processing ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Research And Design ◽  
Speed And Accuracy

Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and accuracy.

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.

Research and Realization for Embedded License Plate Recognition System

2013 ◽  
Vol 411-414 ◽  
pp. 1015-1019
Author(s):  
Yuan Ning ◽  
Yao Wen Liu ◽  
Yan Bin Zhang ◽  
Hao Yuan
Keyword(s):  
Power Consumption ◽  
System Design ◽  
Character Recognition ◽  
Template Matching ◽  
Median Filter ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Design Constraints ◽  
Recognition Phase

In this paper, the embedded license plate recognition system based on TMS320DM642 is researched. During the design, median filter, threshold, and morphology closing operations are used to obtain license plate region, then segmented into disjoint characters for the character recognition phase, where the template matching is used to identify the characters. Embedded License Plate Recognition System, being smaller, has less power consumption with respect to software based LPR systems. The resulting hardware is suitable for applications where cost, compactness, and efficiency are system design constraints.

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.

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