A Comparative Study on Recognition of Degraded Urdu and Devanagari Printed Documents

[1]  N. Sandhya,et al.  A novel local enhancement technique for rebuilding Broken characters in a degraded Kannada script , 2015, 2015 IEEE International Advance Computing Conference (IACC).

[2]  Md. Asraful Haque,et al.  A Slice-based Character Recognition Technique for Handwritten Devanagari Script , 2020 .

[3]  Gernot A. Fink,et al.  PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents , 2016, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR).

[4]  Ashok Kumar Bathla,et al.  Method for Line Segmentation in Handwritten Documents with Touching and Broken Parts in Devanagari Script , 2014 .

[5]  Muhammad Waqas Anwar,et al.  Printed Urdu Nastalique Script Recognition Using Analytical Approach , 2015, 2015 13th International Conference on Frontiers of Information Technology (FIT).

[6]  Imran Siddiqi,et al.  Towards Searchable Digital Urdu Libraries - A Word Spotting Based Retrieval Approach , 2011, 2011 International Conference on Document Analysis and Recognition.

[7]  Mahesh Jangid,et al.  Touching character segmentation of Devanagari script , 2016, ICCCNT.

[8]  Adnan Amin,et al.  Recognition of printed arabic text based on global features and decision tree learning techniques , 2000, Pattern Recognit..

[9]  Christian Wolf,et al.  Document Ink Bleed-Through Removal with Two Hidden Markov Random Fields and a Single Observation Field , 2010, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Laurence Likforman-Sulem,et al.  Overlapping and multi-touching text-line segmentation by Block Covering analysis , 2008, Pattern Analysis and Applications.

[11]  Gerhard Rigoll,et al.  Improved degraded document recognition with hybrid modeling techniques and character n-grams , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  U. Pal,et al.  Recognition of printed Urdu script , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[13]  Adel M. Alimi,et al.  Unsupervised Block Covering Analysis for Text-Line Segmentation of Arabic Ancient Handwritten Document Images , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  Sarmad Hussain,et al.  Segmentation Free Nastalique Urdu OCR , 2010 .

[15]  Ali Alkhalifah,et al.  Urdu text classification using decision trees , 2015, 2015 12th International Conference on High-capacity Optical Networks and Enabling/Emerging Technologies (HONET).

[16]  Donald E. Brown,et al.  HDLTex: Hierarchical Deep Learning for Text Classification , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[17]  Dale Schuurmans,et al.  Augmenting Naive Bayes Classifiers with Statistical Language Models , 2004, Information Retrieval.

[18]  Munish Kumar,et al.  Devanagari ancient documents recognition using statistical feature extraction techniques , 2019, Sādhanā.

[19]  Petrica C. Pop,et al.  Optical character recognition in real environments using neural networks and k-nearest neighbor , 2013, Applied Intelligence.

[20]  Basil K. Papadopoulos,et al.  Local thresholding of degraded or unevenly illuminated documents using fuzzy inclusion and entropy measures , 2019, Evol. Syst..

[21]  Hui Lin,et al.  Global and Local Features Based Classification for Bleed-Through Removal , 2016 .

[22]  Francesco Camastra,et al.  A SVM-based cursive character recognizer , 2007, Pattern Recognit..

[23]  Tanupriya Choudhury,et al.  Analysis of Various Machine Learning Algorithms for Enhanced Opinion Mining Using Twitter Data Streams , 2016, 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE).

[24]  Sarishma,et al.  Information Delivery System for Early Forest Fire Detection Using Internet of Things , 2019 .

[25]  Faisal Shafait,et al.  A segmentation-free approach to Arabic and Urdu OCR , 2013, Electronic Imaging.

[26]  Xiaojie Wang,et al.  Offline Urdu Nastaleeq optical character recognition based on stacked denoising autoencoder , 2017, China Communications.

[27]  Simon Fong,et al.  Hidden Markov Model Based Character Segmentation Factor Applied To Urdu Script , 2015, ICADIWT.

[28]  Rosaria Rossini,et al.  A fuzzy approach to segment touching characters , 2017, Expert Syst. Appl..

[29]  Pramod Sharma,et al.  A Robust OCR for Degraded Documents , 2008 .

[30]  Muhammad Sher,et al.  HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages' character recognition , 2010, Knowl. Based Syst..

[31]  Khalil Khan,et al.  Urdu Character Recognition using Principal Component Analysis , 2012 .

[32]  Anil C. Kokaram,et al.  Degraded Document Bleed-Through Removal , 2011, 2011 Irish Machine Vision and Image Processing Conference.

[33]  Ibrahim S. I. Abuhaiba,et al.  EFFICIENT OCR USING SIMPLE FEATURES AND DECISION TREES WITH BACKTRACKING , 2006 .

[34]  Thomas M. Breuel,et al.  A segmentation-free approach for printed Devanagari script recognition , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[35]  Ajay Rana,et al.  Classification of the Bangla script document using SVM , 2016, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT).

[36]  Theodosios Pavlidis,et al.  A solution to the problem of touching and broken characters , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[37]  Imran Siddiqi,et al.  Classification of Urdu Ligatures Using Convolutional Neural Networks - A Novel Approach , 2017, 2017 International Conference on Frontiers of Information Technology (FIT).

[38]  Shalini Puri,et al.  An efficient Devanagari character classification in printed and handwritten documents using SVM , 2019, Procedia Computer Science.

[39]  Carlo Tomasi,et al.  Manuscript Bleed-through Removal via Hysteresis Thresholding , 2009, 2009 10th International Conference on Document Analysis and Recognition.