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公开(公告)号：US09922263B2
公开(公告)日：2018-03-20
申请号：US14387094
申请日：2013-03-20
Inventor： Tanushyam Chattopadhyay , Arpan Pal , Aniruddha Sinha , Utpal Garain
Abstract: The present disclosure relates to a system and a method for detection of touching characters in a media, characterized by segmentation of adjoining character spaces. In the very first step, an aspect ratio is calculated for each connected component. A candidate touching position of each character is determined by calculating a threshold aspect ratio for each character. Further, a candidate cut column is determined based on a relation between column pixel densities and corresponding length thereof the column in order to segment the touching characters at the candidate cut column.
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Patent Agency Ranking
2.发明授权Method and system for automatic selection of one or more image processing algorithm 有权一种或多种图像处理算法的自动选择方法和系统公开(公告)号：US09275307B2
公开(公告)日：2016-03-01
申请号：US14286303
申请日：2014-05-23
Applicant: Tata Consultancy Services Limited
Inventor： Tanushyam Chattopadhyay , Ramu Vempada Reddy , Utpal Garain
CPC classification number: G06K9/66 , G06K9/34 , G06K9/38 , G06K9/4652 , G06K9/481 , G06K2209/01
Abstract: Disclosed is a method and system for automatic algorithm selection for image processing. The invention discloses the method and system for automatically selecting the correct algorithm(s) for a varying requirement of the image for processing. The selection of algorithm is completely automatic and guided by a plurality of machine learning approaches. The system here is configured to pre-process plurality of images for creating a training data. Next, the test image is extracted, pre-processed and matched for assessing the best possible match of algorithm for processing.
Abstract translation: 公开了用于图像处理的自动算法选择的方法和系统。 本发明公开了用于自动选择用于处理图像的变化需求的正确算法的方法和系统。 算法的选择是完全自动的，并由多种机器学习方法引导。 这里的系统被配置为预处理用于创建训练数据的多个图像。 接下来，提取测试图像，预处理和匹配，以评估用于处理的算法的最佳匹配。
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3.发明申请System and Method for Detection and Segmentation of Touching Characters for OCR 有权OCR触摸字符的检测和分割系统及方法公开(公告)号：US20150086113A1
公开(公告)日：2015-03-26
申请号：US14387094
申请日：2013-03-20
Inventor： Tanushyam Chattopadhyay , Arpan Pal , Aniruddha Sinha , Utpal Garain
IPC: G06K9/34
Abstract: The present disclosure relates to a system and a method for detection of touching characters in a media, characterized by segmentation of adjoining character spaces. In the very first step, an aspect ratio is calculated for each connected component. A candidate touching position of each character is determined by calculating a threshold aspect ratio for each character. Further, a candidate cut column is determined based on a relation between column pixel densities and corresponding length thereof the column in order to segment the touching characters at the candidate cut column.
Abstract translation: 本公开涉及一种用于检测媒体中的触摸人物的系统和方法，其特征在于相邻字符空间的分割。 在第一步中，为每个连接的组件计算宽高比。 通过计算每个字符的阈值长宽比来确定每个字符的候选者触摸位置。 此外，基于列像素密度与列的相应长度之间的关系来确定候选剪切列，以便在候选剪切列处分割触摸字符。
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4.发明申请METHOD AND SYSTEM FOR AUTOMATIC SELECTION OF ONE OR MORE IMAGE PROCESSING ALGORITHM 有权用于自动选择一个或多个图像处理算法的方法和系统公开(公告)号：US20140348420A1
公开(公告)日：2014-11-27
申请号：US14286303
申请日：2014-05-23
Applicant: Tata Consultancy Services Limited
Inventor： Tanushyam Chattopadhyay , Ramu Vempada Reddy , Utpal Garain
CPC classification number: G06K9/66 , G06K9/34 , G06K9/38 , G06K9/4652 , G06K9/481 , G06K2209/01
Abstract: Disclosed is a method and system for automatic algorithm selection for image processing. The invention discloses the method and system for automatically selecting the correct algorithm(s) for a varying requirement of the image for processing. The selection of algorithm is completely automatic and guided by a plurality of machine learning approaches. The system here is configured to pre-process plurality of images for creating a training data. Next, the test image is extracted, pre-processed and matched for assessing the best possible match of algorithm for processing.
Abstract translation: 公开了用于图像处理的自动算法选择的方法和系统。 本发明公开了用于自动选择用于处理图像的变化需求的正确算法的方法和系统。 算法的选择是完全自动的，并由多种机器学习方法引导。 这里的系统被配置为预处理用于创建训练数据的多个图像。 接下来，提取测试图像，预处理和匹配，以评估用于处理的算法的最佳匹配。
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5.发明授权公开(公告)号：US12038949B2
公开(公告)日：2024-07-16
申请号：US18383930
申请日：2023-10-26
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Arpan Pal , Soumadeep Saha , Utpal Garain
IPC: G06F16/28
CPC classification number: G06F16/285
Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model. The disclosed method is used for electrocardiogram classification, shape classification and so on.
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6.发明授权公开(公告)号：US11475341B2
公开(公告)日：2022-10-18
申请号：US16179771
申请日：2018-11-02
Applicant: Tata Consultancy Services Limited
Inventor： Ishan Sahu , Snehasis Banerjee , Tanushyam Chattopadhyay , Arpan Pal , Utpal Garain
Abstract: Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.
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