Slant estimation and core-region detection for handwritten Latin words

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.

• Slant is estimated by the orientation, the height and position of each fragment.
• The core-region of the word is detected with the use of novel black run profiles.
• Extensive experiments have been done in Datasets with real and synthetic data.
• We compared our methodology with 4 slant estimation algorithms of all categories.
• We compared our core-region detection methodology with 2 algorithms.