A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object count/area graphs for the evaluation of object detection and segmentation algorithms
International Journal on Document Analysis and Recognition
Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Truthing for Pixel-Accurate Segmentation
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
PixLabeler: User Interface for Pixel-Level Labeling of Elements in Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Multi-script and multi-oriented text localization from scene images
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Multi-script and multi-oriented text localization from scene images
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Benchmarking recognition results on camera captured word image data sets
Proceeding of the workshop on Document Analysis and Recognition
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
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This paper describes a semi-automatic tool for annotation of multi-script text from natural scene images. To our knowledge, this is the maiden tool that deals with multi-script text or arbitrary orientation. The procedure involves manual seed selection followed by a region growing process to segment each word present in the image. The threshold for region growing can be varied by the user so as to ensure pixel-accurate character segmentation. The text present in the image is tagged word-by-word. A virtual keyboard interface has also been designed for entering the ground truth in ten Indic scripts, besides English. The keyboard interface can easily be generated for any script, thereby expanding the scope of the toolkit. Optionally, each segmented word can further be labeled into its constituent characters/symbols. Polygonal masks are used to split or merge the segmented words into valid characters/symbols. The ground truth is represented by a pixel-level segmented image and a '.txt' file that contains information about the number of words in the image, word bounding boxes, script and ground truth Unicode. The toolkit, developed using MATLAB, can be used to generate ground truth and annotation for any generic document image. Thus, it is useful for researchers in the document image processing community for evaluating the performance of document analysis and recognition techniques. The multi-script annotation toolokit (MAST) is available for free download.