Pattern Recognition of Handwritten Text Based on Bayes Algorithm

2011 ◽  
pp. 442-447
Author(s):  
Jianming Cui
Keyword(s):  
Pattern Recognition ◽  
Handwritten Text ◽  
Bayes Algorithm

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 Japanese Letter Pattern Recognition Application with Tesseract Engine

2015 ◽  
Vol 4 (2) ◽  
pp. 8
Author(s):  
Akhmad Imam Fahrizal ◽  
Ahmad Subhan Yazid ◽  
Shofwatul Uyun
Keyword(s):  
Pattern Recognition ◽  
Digital Image ◽  
Recognition System ◽  
Image Resolution ◽  
Training Data ◽  
Font Size ◽  
Handwritten Text ◽  
Testing Criteria ◽  
Success Percentage ◽  
Letter Pattern

Digital image processing is a field that is being cultivated by many researchers at this time because it is interesting to apply to various activities, both analysis and production activities. One branch of the digital image is pattern recognition. This study uses Tesseract as a tool to recognize patterns from Hiragana letters. This study was conducted to find out how much Tesseract was able to recognize a Japanese text and handwritten text. This study uses 1 image as training data containing 74 Hiragana letters which are processed through training for each letter. This study has several testing criteria based on font size and resolution to find the best results in pattern recognition. This pattern recognition system is able to do data training and recognize 74 Hiragana letters using the Tesseract Engine. The system can also recognize images with the best success percentage of 98.24% with an image resolution of 200dpi (dots per inch) at size 18. This system can also recognize handwritten images with the best percentage of success of 90% with 200dpi image resolution.

scholarly journals Quantitative Analysis Powered by Naïve Bayes Classifier Algorithm to Data-Related Publications Social-Scientific Network

2020 ◽  
Vol 8 (6) ◽  
pp. 205-217
Author(s):  
Tobias Sombra ◽  
Rose Santini ◽  
Emerson Morais ◽  
Walmir Couto ◽  
Alex Zissou ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Exploratory Data Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Feature Weighting ◽  
Research Network ◽  
Bayes Classifier ◽  
Exploratory Data ◽  
Bayes Algorithm ◽  
Social Scientific

Quantitative evaluation of a dataset can play an important role in pattern recognition of technical-scientific research involving behavior and dynamics in social networks. As an example, are the adaptive feature weighting approaches by naive Bayes text algorithm. This work aims to present an exploratory data analysis with a quantitative approach that involves pattern recognition using the Mendeley research network; to identify logics given the popularity of document access. To better analyze the results, the work was divided into four categories, each with three subcategories, that is, five, three, and two output classes. The name for these categories came up due to data collection, which also presented documents with open access, dismembering proceedings, and journals for two more categories. As a result, the performance for the test examples showed a lower error rate related to the subcategory two output classes in the criterion of popularity by using the naive Bayes algorithm in Mendeley.

scholarly journals Handwritten Text Recognition using Machine Learning Techniques in Application of NLP

2019 ◽  
Vol 9 (2) ◽  
pp. 1394-1397
Keyword(s):  
Pattern Recognition ◽  
Handwriting Recognition ◽  
Machine Learning Techniques ◽  
Text Recognition ◽  
Spatial Form ◽  
Process Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Bank Checks ◽  
Cursive Script

Handwriting Detection is a technique or ability of a Computer to receive and interpret intelligible handwritten input from source such as paper documents, touch screen, photo graphs etc. Handwritten Text recognition is one of area pattern recognition. The purpose of pattern recognition is to categorizing or classification data or object of one of the classes or categories. Handwriting recognition is defined as the task of transforming a language represented in its spatial form of graphical marks into its symbolic representation. Each script has a set of icons, which are known as characters or letters, which have certain basic shapes. The goal of handwriting is to identify input characters or image correctly then analyzed to many automated process systems. This system will be applied to detect the writings of different format. The development of handwriting is more sophisticated, which is found various kinds of handwritten character such as digit, numeral, cursive script, symbols, and scripts including English and other languages. The automatic recognition of handwritten text can be extremely useful in many applications where it is necessary to process large volumes of handwritten data, such as recognition of addresses and postcodes on envelopes, interpretation of amounts on bank checks, document analysis, and verification of signatures. Therefore, computer is needed to be able to read document or data for ease of document processing.

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.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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