Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way.  proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lor) and then recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone, where characters are mostly touching, are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters in the middle zone. Next, the residue components, if any, in upper and lor zones are obtained in a character boundary then the components are combined with the character to achieve the final word level recognition. Water reservoir-based properties have been integrated in this framework to improve the zone segmentation and character boundary detection defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features have been compared with other existing features and found robust for Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition.
['Partha Roy', 'Ayan Kumar Bhunia', 'Ayan Das', 'Prasenjit Dey', 'Umapada Pal']
I am writing a research paper on a idea HMM-based Indic handwritten word recognition using zone segmentation
[{"from": "human", "value": "I am writing a research paper on a idea HMM-based Indic handwritten word recognition using zone segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way.  proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lor) and then recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone, where characters are mostly touching, are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters in the middle zone. Next, the residue components, if any, in upper and lor zones are obtained in a character boundary then the components are combined with the character to achieve the final word level recognition. Water reservoir-based properties have been integrated in this framework to improve the zone segmentation and character boundary detection defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features have been compared with other existing features and found robust for Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition."}]