A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation and Improvement of Slant Estimation for Handwritten Words
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Local Slant Estimation for Handwritten English Words
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Adaptive degraded document image binarization
Pattern Recognition
Word Slant Estimation Using Non-horizontal Character Parts and Core-Region Information
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
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In this paper, we present a new technique that estimates the slant in handwritten words while a new word core-region detection method is introduced as part of the proposed technique. The proposed core-region detection algorithm can be also used independently to detect the upper and lower baselines of a word. Our method takes advantage of the orientation of the non- horizontal strokes of Latin characters as well as their location regarding to the word's core-region. As a first step, the word core-region is detected with the use of novel reinforced horizontal black run profiles which permits to detect the core-region scan lines more accurately. Then, the near-horizontal parts of the document word are extracted and the orientation and the height of non-horizontal remaining fragments as well as their location in relation to the word's core-region are calculated. Word slant is estimated taking into consideration the orientation and the height of each fragment while an additional weight is applied if a fragment is partially outside the core-region of the word which indicates that this fragment corresponds to a part of the character stroke that has a significant contribution to the overall word slant and should by definition be vertical to the orientation of the word. Extensive experimental results prove the efficiency of the proposed slant estimation method compared to current state-of-the-art algorithms.