Automated entry system for printed documents
Pattern Recognition
Determination of the Script and Language Content of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Script Identification From Document Images Using Cluster-Based Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Texture Features and Their Use in Automatic Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm for text page up/down orientation determination
Pattern Recognition Letters
A nearest-neighbor chain based approach to skew estimation in document images
Pattern Recognition Letters
A fast orientation and skew detection algorithm for monochromatic document images
Proceedings of the 2005 ACM symposium on Document engineering
Automatic image orientation determination with natural image statistics
Proceedings of the 13th annual ACM international conference on Multimedia
Script and language identification in degraded and distorted document images
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Automatic image orientation detection
IEEE Transactions on Image Processing
Orientation detection of major Indian scripts
Proceedings of the International Workshop on Multilingual OCR
Recognition driven page orientation detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
| Hi-index | 0.00 |
This paper presents an automatic orientation detection and categorization technique that is capable of detecting the orientation of multilingual documents with arbitrary skew and categorizing document images according to the underlying languages. We carry out orientation detection and categorization through document vectorization, which encodes document orientation and language information and converts each document image into an electronic document vector through the exploitation of the density and distribution of vertical component runs. For each language of interest, a pair of vector templates is first constructed through a training process. Orientation and category of the query image are then determined based on distances between the query document vector and the constructed vector templates. Experiments over 492 testing document images show that the average orientation detection and categorization rates reach up to 97.56% and 99.59%, respectively.