Implementation of Preprocessing and Segmentation Methodologies for Word Spotting in Ancient Handwritten Text Documents

2021 ◽  
Vol 8 (2) ◽  
Keyword(s):  
Text Documents ◽  
Word Spotting ◽  
Handwritten Text

scholarly journals Unconstrained Handwritten Text Line Segmentation for Kannada Language

2019 ◽  
Vol 8 (12) ◽  
pp. 953-956
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Text Line ◽  
Connected Component ◽  
Horizontal Projection ◽  
Text Documents ◽  
Handwritten Text ◽  
Kannada Language ◽  
System Separation ◽  
Line Segmentation

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

A Fuzzy Matching based Image Classification System for Printed and Handwritten Text Documents

2020 ◽  
Vol 13 (2) ◽  
pp. 155-194
Author(s):  
Shalini Puri ◽  
Satya Prakash Singh
Keyword(s):  
Image Classification ◽  
Classification System ◽  
Word Association ◽  
Computation Time ◽  
Document Classification ◽  
Efficient Manner ◽  
Text Documents ◽  
Handwritten Text ◽  
Fuzzy Matching ◽  
Character Classification

This article proposes a bi-leveled image classification system to classify printed and handwritten English documents into mutually exclusive predefined categories. The proposed system follows the steps of preprocessing, segmentation, feature extraction, and SVM based character classification at level 1, and word association and fuzzy matching based document classification at level 2. The system architecture and its modular structure discuss various task stages and their functionalities. Further, a case study on document classification is discussed to show the internal score computations of words and keywords with fuzzy matching. The experiments on proposed system illustrate that the system achieves promising results in the time-efficient manner and achieves better accuracy with less computation time for printed documents than handwritten ones. Finally, the performance of the proposed system is compared with the existing systems and it is observed that proposed system performs better than many other systems.

scholarly journals Probabilistic multi-word spotting in handwritten text images

2018 ◽  
Vol 22 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Alejandro H. Toselli ◽  
Enrique Vidal ◽  
Joan Puigcerver ◽  
Ernesto Noya-García
Keyword(s):  
Word Spotting ◽  
Handwritten Text ◽  
Text Images

Processing of binary images of handwritten text documents

1996 ◽  
Vol 29 (7) ◽  
pp. 1161-1177 ◽  
Author(s):  
I.S.I Abuhaiba ◽  
M.J.J Holt ◽  
S Datta
Keyword(s):  
Text Documents ◽  
Binary Images ◽  
Handwritten Text

scholarly journals Writer Identification in Old Handwritten Music Scores

2011 ◽  
pp. 27-63 ◽  
Author(s):  
Alicia Fornés ◽  
Josep Lladós ◽  
Gemma Sánchez ◽  
Horst Bunke
Keyword(s):  
Active Area ◽  
Connected Components ◽  
Textural Features ◽  
Symbol Recognition ◽  
Writer Identification ◽  
Text Documents ◽  
The Third ◽  
Handwritten Text ◽  
Area Of Study

Writer identification in handwritten text documents is an active area of study, whereas the identification of the writer of graphical documents is still a challenge. The main objective of this work is the identification of the writer in old music scores, as an example of graphic documents. The writer identification framework proposed combines three different writer identification approaches. The first one is based on the use of two symbol recognition methods, robust in front of hand-drawn distortions. The second one generates music lines and extracts information about the slant, width of the writing, connected components, contours and fractals. The third approach generates music texture images and computes textural features. The high identification rates obtained demonstrate the suitability of the proposed ensemble architecture. To the best of our knowledge, this work is the first contribution on writer identification from images containing graphical languages.

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