This paper presents an insight into the state-of-art
in handwriting recognition systems and describes the evolution
and progress in the field. An in-depth literature survey of Indic
script recognition systems for Bangla, Devnagari, Gurumukhi,
Kannada, Malayalam, tamil, and Urdu is presented. This study
focuses on multitude of feature and classification techniques
giving an insight into the efficacy of these methods for the
various Indic scripts. The review explores new opportunities and
challenges for future research in computational research areas
e.g. imaging sciences.
Jayashree Rajesh Prasad : graduated in Computer Science and Engineering
from North Maharashtra University in 1996 and completed M.E. in
Computer Engineering from Pune University in 2004. She pursued Ph.D. in
Computer Science and engineering from Swami Ramananda Tirtha
University, Nanded in 2014. She has a research project “Conversion of
Gujrati Script to Speech”, funded by BCUD (University of Pune) to her
credit. She works with Sinhgad College of Engineering, Pune. Her research
interests are in the field of Soft Computing, pattern recognition and image
processing. She is Life member of Computer Society of India, Life Member
of Indian Society for Technical Education, Member of IAENG
(International Association of Engineers) and Member of IACSIT
(International Association of Computer Science and Information
Technology).
Classification
Directional pattern matching
HMM
SVM
Statistical pattern recognition
structural pattern
recognition
The author has surveyed majority of approaches to
handwriting recognition in late nineties. A detailed study
on most of the western handwriting and an extensive
survey of recognition techniques on Indic scripts is done,
providing an overview of the state-of-art research in the
field. There is scope for future research to design systems
using less information on the drawing but using much
more priory knowledge. Instead of the serial or
hierarchical organization the further research needs
directions towards organizing the modules alternatively.
[1] G. Lorette, “Handwriting Recognition or Reading
Situation At the Dawn Of The 3rd Millennium,” In:
Advances in Handwriting Recognition, World
Scientific Publications, pp. 3-13, 1997. [2] Thien M. Ha and H. Bunke, “Image Processing
Methods for Document Image Analysis”, In: Handbook
of Character Recognition and Document Image
Analysis, pp.1-47, 1997.
[3] Ulrich and Jurgen, “Pattern Classification Techniques
Base on Function Approximation”, In: Handbook of
Character Recognition and Document Image Analysis,
World Scientific Publishing Company, pp. 49-78.,
1997.
[4] H. Yasuada, K. Takahashi, and T. Matsumoto, “Online
Handwriting Recognition by Discrete HMM with
Fast Learning”, In: Advances in Handwriting
Recognition, World Scientific Publications, pp. 19-28,
1997.
[5] G. Rigoll, A. Kosmala, and D. Willet, “A Systematic
Comparison of Advanced Modeling Techniques For
Very Large Vocabulary On-line Cursive Handwriting
Recognition”, In: Advances in Handwriting
Recognition, World Scientific Publications, pp. 69-
78., 1997.
[6] Kam-Faichan and Dit-Yan Yeung, “A Simple Yet
Robust Structural Approach For On-line Handwritten
Alphanumeric Character Recognition”, In: Advances
in Handwriting Recognition, World Scientific
Publications, pp. 39-48, 1997.
[7] Giovanni Seni and John Seybold, “Diacritical
Processing Using Efficient Accounting Procedures in a
Forward Search”, In: Advances in Handwriting
Recognition, World Scientific Publications, pp. 49-58,
1997.
[8] L. Pasquer, Anqueti and Lorette, “Coherent
Knowledge Source Integration through Perceptual
Cycle Framework for Handwriting Recognition”, In:
Advances in Handwriting Recognition, World
Scientific Publications, pp. 59-68, 1997.
[9] Dori, Haralick, Phillips, Buchman, and Ross, “The
Representation of Document Structure: A Generic
Object-Process Analysis”, In: Handbook of Character
Recognition and Document Image Analysis, World
Scientific Publishing Company, pp.421-456, 1997.
[10] H. Bunke, P.S.P. Wang, “Handbook of Character
Recognition and Document Image Analysis”, World
Scientific Publishing Co. Private Limited, ISBN- 981-
02-2270-X, 1997.
[11] Seong-Whan Lee, “Advances in Handwriting
Recognition”, In: Series in Machine Perception
Artificial Intelligence, Volume 34, Word Scientific
Publications, ISBN- 981-02-3715-4, 1999.
[12] Xian Wang, Govindraju and Srihari, “Holistic
Recognition of Touching Digits”, In: Advances in
Handwriting Recognition, World Scientific
Publications, pp. 359-369, 1997.
[13] Diasuke, Keiji, “A New Numeral String Recognition
Method Using Character Touching Type Verification”,
In: Advances in Handwriting Recognition, World
Scientific Publications, pp. 416-425, 1997.
[14] Jie Zhou, Gan, and Suen, “Quantum Neural Network
in Recognition of Handwritten Numerals”, In:
Advances in Handwriting Recognition, World
Scientific Publications, pp. 3, 1997.
[15] Venu Govindaraju and Srirangaraj Setlur (Eds.),
“Guide to OCR for Indic Scripts, Document
Recognition and Retrieval”, In: Advances in Pattern
Recognition, ISBN 978-1-84800-329-3, Springer
London Dordrecht Heidelberg New York, 2009.
[16] C. V. Jawahar, Anand Kumar, A. Phaneendra and K.
J. Jinesh, “Building Data Sets for Indian Language
OCR Research", In: Guide to OCR for Indic Scripts,
Document Recognition and Retrieval”, ISBN 978-1-
84800-329-3, Springer London Dordrecht Heidelberg
New York, 2009.
[17] B.B. Chaudhari, “On OCR of Major Indian Scripts:
Bangla and Devnagari”, In: Guide to OCR for Indic
Script, Document Recognition and Retrieval”, ISBN:
978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[18] G. S. Lehal, “A complete Machine –Printed
Gurumukhi OCR System”, In: Guide to OCR for Indic
Scripts Document Recognition and Retrieval”, ISBN:
978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[19] Jignesh Dholakiya, Atul Negi and S. Ramamohan, “
Progress in Gujrati Document Processing and
Character Recognition”, In: Guide to OCR for Indic
Scripts Document Recognition and Retrieval”, ISBN:
978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[20] R. S. Umesh, Peeeta Basa Patil and A. G.
Ramakrishnan, “Design of Bilingual Kannada-English
OCR”, In: Guide to OCR for Indic Scripts Document
Recognition and Retrieval”, ISBN: 978-84800-329-3,
Springer London Dordrecht Heidelberg New York,
2009.
[21] N. V. Neeba, Anoop Namboodiri, C.V. Jawahar and
P.J. Narayanan, “Recognition of Malayalam
Documents”, In: Guide to OCR for Indic Scripts
Document Recognition and Retrieval, ISBN: 978-
84800-329-3, Springer London Dordrecht Heidelberg
New York, 2009.
[22] Aparna Kokku and Srinivasa Chakravarthy, “A
complete OCR System for Tamil Magazine
Documents”, In: Guide to OCR for Indic Scripts
Document Recognition and Retrieval, ISBN: 978-
84800-329-3, Springer London Dordrecht Heidelberg
New York, 2009.
[23] Omar Mukhtar, Srirangaraj Setlur and Venu
Govindaraju, “Experiments in Urdu Text
Recognition”, In: Guide to OCR for Indic Scripts
Document Recognition and Retrieval, ISBN: 978-
84800-329-3, Springer London Dordrecht Heidelberg
New York, 2009.
[24] Prem Natarajan, Ehry MacRostie and Michael
Decerbo, “The BBN Byblos Hindi OCR System”, In:
Guide to OCR for Indic Scripts Document Recognition
and Retrieval, ISBN: 978-84800-329-3, Springer
London Dordrecht Heidelberg New York, 2009.[25] Mudit Agrawal, Huanfeng Ma and David Doermann,
“Generalization of Hindi OCR Using Adaptive
Segmentation and Font Files”, In: Guide to OCR for
Indic Scripts Document Recognition and Retrieval,
ISBN: 978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[26] A. Bharath and Sriganesh Madhavnath, “Online
Handwriting Recognition for Indic Scripts”, In: Guide
to OCR for Indic Scripts Document Recognition and
Retrieval, ISBN: 978-84800-329-3, Springer London
Dordrecht Heidelberg New York, 2009.
[27] Peter M. Scharf and Malcolm Hyman, “Enhancing
Access to Primary Cultural Heritage Materials of
India”, In: Guide to OCR for Indic Scripts Document
Recognition and Retrieval, ISBN: 978-84800-329-3,
Springer London Dordrecht Heidelberg New York,
2009.
[28] Zhixin Shi, Srirangaraj Setlur and Venu Govindaraju,
“Digital Image Enhancement of Indic Historical
Manuscripts”, In: Guide to OCR for Indic Scripts
Document Recognition and Retrieval, ISBN: 978-
84800-329-3, Springer London Dordrecht Heidelberg
New York, 2009.
[29] Gaurav Harit, Santanu Chaudhari and Ritu Garg,
“CFG-Based Compression and Retrieval of Document
Images in Indian Scripts”, In: Guide to OCR for Indic
Scripts Document Recognition and Retrieval, ISBN:
978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[30] Anurag Bhardwaj, Srirangaraj Setlur and Venu
Govindaraju, “Word Spotting for Indic Documents to
Facilitate Retrieval”, In: Guide to OCR for Indic
Scripts Document Recognition and Retrieval, ISBN:
978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[31] Prasanjit Majumdar and Mandar Mitra, “Indian
Language Information Retrieval”, In: Guide to OCR
for Indic Scripts Document Recognition and Retrieval,
ISBN: 978-84800-329-3, Springer London Dordrecht
Heidelberg New York, 2009.
[32] L. Vuurpijl and L. Schomaker, Multiple-agent
Architecture for the Classification of Handwritten
Text, In: Series in Machine Perception Artificial
Intelligence, Volume 34, Word Scientific Publications,
ISBN- 981-02-3715-4, 1999.
[33] P. M. Lallican and C. Viard-Gaudin, Offline
Handwriting Modeling as a Trajectory Tracking
Problem, In: Series in Machine Perception Artificial
Intelligence, Volume 34, Word Scientific Publications,
ISBN- 981-02-3715-4, 1999.
[34] D. Mangalagiu and M. Weinfeld, Tree Search
Technologies for the Optimization of the k Nearest
Neighbors Algorithm, In: Series in Machine
Perception Artificial Intelligence, Volume 34, Word
Scientific Publications, ISBN- 981-02-3715-4, 1999.
[35] J. Laaksonen, V. Vuori, E. Oja and J. Kangas,
Adaptation of Prototype Sets in On-line Recognition of
Isolated Handwritten atin Characters, In: Series in
Machine Perception Artificial Intelligence, Volume
34, Word Scientific Publications, ISBN- 981-02-3715-
4, 1999.
[36] K. Iwata, M. Okamoto, K. Kato and K. Yamamoto,
Integration of Dictionaries in the Character
Recognition by Relaxation Matching, In: Series in
Machine Perception Artificial Intelligence, Volume
34, Word Scientific Publications, ISBN- 981-02-3715-
4, 1999.
[37] L. Feng, Y. Y. Tang, Q. Sun and L. H. Yang, Number
Theoretic Transform Approach to Handwriting
Recognition, In: Series in Machine Perception
Artificial Intelligence, Volume 34, Word Scientific
Publications, ISBN- 981-02-3715-4, 1999.
