Principles of artificial intelligence
Principles of artificial intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trainable Script Identification Strategies for Indian Languages
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Script Line Separation from Indian Multi-Script Documents
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Automatic Identification of English, Chinese, Arabic, Devnagari and Bangla Script Line
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Multi-Script Line identification from Indian Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A System towards Indian Postal Automation
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Gabor Feature Extraction for Character Recognition: Comparison with Gradient Feature
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A System for Indian Postal Automation
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Handwritten Numeral Recognition of Six Popular Indian Scripts
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
A generalised framework for script identification
International Journal on Document Analysis and Recognition
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Gabor filters-based feature extraction for character recognition
Pattern Recognition
Handwritten bangla digit recognition using classifier combination through DS technique
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
On recognition of handwritten bangla characters
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Bangla/English script identification based on analysis of connected component profiles
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Script identification from indian documents
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
The optical character recognition of Urdu-like cursive scripts
Pattern Recognition
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Recognition of numeric postal codes in a multi-script environment is a classical problem in any postal automation system. In such postal documents, determination of the script of the handwritten postal codes is crucial for subsequent invocation of the digit recognizers for respective scripts. The current framework attempts to infer about the script of the numeric postal code without having any bias from the script of the textual address part of the rest of the address block, as they might differ in a potential multi-script environment. Scope of the current work is to recognize the postal codes written in any of the four popular scripts, viz., Latin, Devanagari, Bangla and Urdu. For this purpose, we first implement a Hough transformation based technique to localize the postal-code blocks from structured postal documents with defined address block region. Isolated handwritten digit patterns are then extracted from the localized postal-code region. In the next stage of the developed framework, similar shaped digit patterns of the said four scripts are grouped in 25 clusters. A script independent unified pattern classifier is then designed to classify the numeric postal codes into one of these 25 clusters. Based on these classification decisions a rule-based script inference engine is designed to infer about the script of the numeric postal code. One of the four script specific classifiers is subsequently invoked to recognize the digit patterns of the corresponding script. A novel quad-tree based image partitioning technique is also developed in this work for effective feature extraction from the numeric digit patterns. The average recognition accuracy over ten-fold cross validation of results for the support vector machine (SVM) based 25-class unified pattern classifier is obtained as 92.03%. With randomly selected six-digit numeric strings of four different scripts; an average of 96.72% script inference accuracy is achieved. The average of tenfold cross-validation recognition accuracies of the individual SVM classifiers for the Latin, Devanagari, Bangla and Urdu numerals are observed as 95.55%, 95.63%, 97.15% and 96.20%, respectively.