A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skew Angle Detection of Digitized Indian Script Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
Segmentation of Bangla Handwritten Text into Characters by Recursive Contour Following
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Statistically Based, Highly Accurate Text-Line Segmentation Method
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Arabic Hand-Written Text-Line Extraction
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Rule-based Middle-level Character Detection for Simplifying Thai Document Layout Analysis
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Handwritten Arabic text line segmentation using affinity propagation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A new scheme for unconstrained handwritten text-line segmentation
Pattern Recognition
Lampung - a new handwritten character benchmark: database, labeling and recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
An improved contour-based thinning method for character images
Pattern Recognition Letters
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
ACM Transactions on Asian Language Information Processing (TALIP)
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To take care of variability involved in the writing style ofdifferent individuals in this paper we propose a robustscheme to segment unconstrained handwritten Banglatexts into lines, words and characters. For linesegmentation, at first, we divide the text into verticalstripes. Stripe width of a document is computed bystatistical analysis of the text height in the document.Next we determine horizontal histogram of these stripesand the relationship of the minimal values of thehistograms is used to segment text lines. Based onvertical projection profile lines are segmented intowords. Segmentation of characters from handwrittenword is very tricky as the characters are seldomvertically separable. We use a concept based on waterreservoir principle for the purpose. Here we, at first,identify isolated and connected (touching) characters ina word. Next touching characters of the word aresegmented based on the reservoir base area points andstructural feature of the component.