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 CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

2020 ◽  
Vol 6 (5) ◽  
pp. 32 ◽  
Author(s):  
Yekta Said Can ◽  
M. Erdem Kabadayı
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Text Recognition ◽  
Historical Documents ◽  
Layout Analysis ◽  
Page Segmentation ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Different Types ◽  
Archival Documentation

Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.

Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition

2019 ◽  
Vol 10 (4) ◽  
pp. 26-45 ◽  
Author(s):  
Mohamed Elleuch ◽  
Monji Kherallah
Keyword(s):  
Character Recognition ◽  
State Of The Art ◽  
Handwriting Recognition ◽  
Image Data ◽  
Text Recognition ◽  
Deep Belief Networks ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Accuracy Rates ◽  
Hierarchical Representations

In recent years, deep learning (DL) based systems have become very popular for constructing hierarchical representations from unlabeled data. Moreover, DL approaches have been shown to exceed foregoing state of the art machine learning models in various areas, by pattern recognition being one of the more important cases. This paper applies Convolutional Deep Belief Networks (CDBN) to textual image data containing Arabic handwritten script (AHS) and evaluated it on two different databases characterized by the low/high-dimension property. In addition to the benefits provided by deep networks, the system is protected against over-fitting. Experimentally, the authors demonstrated that the extracted features are effective for handwritten character recognition and show very good performance comparable to the state of the art on handwritten text recognition. Yet using Dropout, the proposed CDBN architectures achieved a promising accuracy rates of 91.55% and 98.86% when applied to IFN/ENIT and HACDB databases, respectively.

scholarly journals Enhancing Predictability of Handwritten Document Content using HTR and Word Substitution

2020 ◽  
Vol 6 (7) ◽  
pp. 15-18
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Distance Analysis ◽  
Blur Detection ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Handwritten Document ◽  
Positive Results ◽  
Word Completion

Handwritten Text Recognition (HTR) can become progressively abysmal when the documents are damaged with smudges, blemishes and blurs. Recognition of such documents is a challenging task. We, therefore propose a system to identify textual handwritten content in documents where the state-of-the-art Optical Character Recognition (OCR) existing at its full extent performs with low accuracy. By introducing word substitution using character and distance analysis for spell checking and word completion in such areas for giving out more accurate results using a word corpus, we improved our prediction results especially in cases where the OCR is prone to predict false positives on the smudge areas predominantly. Blur detection on every word before segmentation is also substituted with a new word by our OCR algorithm to avoid false positive results and are instead substituted with suitable words. This methodology is far more convenient and reliable since even state-of-the-art HTR technologies do not have more than 71% accuracy. The accuracy of the predicted test is measured using the text similarity metric - Fuzzy Token Set Ratio (FTSR)

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 One-Word Answer Correction using Deep Learning Models and OCR

2020 ◽  
Vol 9 (2) ◽  
pp. 679-682
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Educational Organizations ◽  
Learning Models ◽  
Image Captioning ◽  
New Approach ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Text Features

Examinations/Assessments are a way to assess the understanding of a student on a particular subject. Even today many educational organizations prefer to conduct exams by offline mode (pen and paper). And evaluating them is a timeconsuming process. There is no effectual model to evaluate Offline descriptive answers automatically. The traditional method involves staff assessing the content manually. In place of this process, a new approach using image captioning by using deep learning algorithms can be implemented. Handwritten Text Recognition (HTR) can be used to evaluate descriptive answers. One-word Answers captured as images are pre-processed to extract the text features using deep learning models and pytesseract. This paper presents a comparison between the CNNRNN hybrid model and Optical Character Recognition (OCR) to predict a score for one-word answers.

scholarly journals Various Models for the Conversion of Handwritten Text to Digital Text

2021 ◽  
Vol 9 (VI) ◽  
pp. 2894-2899
Author(s):  
Bhavyasri Maddineni
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Handwriting Recognition ◽  
Machine Learning Techniques ◽  
Digital Text ◽  
Handwritten Text ◽  
Character Extraction ◽  
Learning Techniques ◽  
Handwritten Text Recognition ◽  
Writer Recognition

Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the traditional techniques are Character extraction, Character recognition, and Feature extraction, while the modern techniques are segmenting the lines for recognition, machine learning techniques, convolution neural networks, and recurrent neural networks. There are various applications for the HTR/HWR such as the Online recognition, Offline Recognition, Signature verification, Postal address interpretation, Bank-Cheque processing, Writer recognition and these are considered to be the active areas of research. An effective HTR/HWR is therefore needed for the above stated applications. During this project our objective is to find and develop various models of the purpose.

scholarly journals Label Transcript is Done – Now what do we do with that Data?

2018 ◽  
Vol 2 ◽  
pp. e27055
Author(s):  
Robert Cubey ◽  
Elspeth Haston ◽  
Sally King
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Linked Data ◽  
Data Stream ◽  
Text Recognition ◽  
Botanic Garden ◽  
Optical Character ◽  
Natural History Collection ◽  
Handwritten Text ◽  
Handwritten Text Recognition

The transcription of natural history collection labels is occurring via a variety of different methods – in-house curators, commercial operations, citizen scientists, visiting researchers, linked data, optical character recognition (OCR), handwritten text recognition (HTR), etc., but what can a collections data manager do with this flood of data? There are a whole raft of questions around this incoming data stream - who values it, who needs it, where is it stored, where is it displayed, who has access to it, etc. This talk plans to address these topics with reference to the Royal Botanic Garden Edinburgh herbarium dataset.

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 WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

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.

Sign in / Sign up

Export Citation Format

Share Document