A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid neural network model in handwritten word recognition
Neural Networks
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
Analysis of Segmentation Performance on the CEDAR Benchmark Database
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Contour Code Feature Based Segmentation For Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Automatic Segmentation and Recognition System for Handwritten Dates on Canadian Bank Cheques
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Segmentation of Connected Handwritten Numerals by Graph Representation
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Word segmentation of handwritten text using supervised classification techniques
Applied Soft Computing
Fuzzy technique based recognition of handwritten characters
Image and Vision Computing
Holistic cursive word recognition based on perceptual features
Pattern Recognition Letters
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Automatic scoring of short handwritten essays in reading comprehension tests
Artificial Intelligence
Retrieval of machine-printed Latin documents through Word Shape Coding
Pattern Recognition
Pattern Recognition Letters
Filtering segmentation cuts for digit string recognition
Pattern Recognition
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Morphological preprocessing method to thresholding degraded word images
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Signature verification (SV) toolbox: Application of PSO-NN
Engineering Applications of Artificial Intelligence
A metasynthetic approach for segmenting handwritten Chinese character strings
Pattern Recognition Letters
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiagents to Separating Handwritten Connected Digits
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Iterative Cross Section Sequence Graph for Handwritten Character Segmentation
IEEE Transactions on Image Processing
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
Segmentation of connected handwritten digits using Self-Organizing Maps
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A novel segment confidence-based binary segmentation (SCBS) for cursive handwritten words is presented in this paper. SCBS is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, SCBS is an unordered segmentation approach. SCBS is repetition of binary segmentation and fusion of segment confidence. Each repetition generates only one final segmentation point. The binary segmentation module is a contour tracing algorithm to find a segmentation path to divide a segment into two segments. A set of segments before binary segmentation is called pre-segments, and a set of segments after binary segmentation is called post-segments. SCBS uses over-segmentation technique to generate suspicious segmentation points on pre-segments. On each suspicious segmentation point, binary segmentation is performed and the highest fusion value is recorded. If the highest fusion value is greater than the one of pre-segments, the suspicious segmentation point becomes the final segmentation point for the iteration. If not, no more segmentation is required. Segment confidence is obtained by fusing mean character, lexical and shape confidences. The proposed approach has been evaluated on local and benchmark (CEDAR) databases.