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1. 发明授权

US06249605B1 Key character extraction and lexicon reduction for cursive text recognition 失效
标题翻译：草图文本识别的关键字符提取和词典缩减
公开(公告)号：US06249605B1
公开(公告)日：2001-06-19
申请号：US09152587
申请日：1998-09-14
申请人： Jianchang Mao , Matthias Zimmerman
发明人： Jianchang Mao , Matthias Zimmerman
IPC分类号： G06K918
CPC分类号： G06K9/00872
摘要： A method, apparatus, and article of manufacturing employing lexicon reduction using key characters and a neural network, for recognizing a line of cursive text. Unambiguous parts of a cursive image, referred to as “key characters,” are identified. If the level of confidence that a segment of a line of cursive text is a particular character is higher than a threshold, and is also sufficiently higher than the level of confidence of neighboring segments, then the character is designated as a key character candidate. Key character candidates are then screened using geometric information. The key character candidates that pass the screening are designated key characters. Two-stages of lexicon reduction are employed. The first stage of lexicon reduction uses a neural network to estimate a lower bound and an upper bound of the number of characters in a line of cursive text. Lexicon entries having a total number of characters outside of the bounds are eliminated. For the second stage of lexicon reduction, the lexicon is further reduced by comparing character strings using the key characters, with lexicon entries. For each of the key characters in the character strings, it is determined whether there is a mismatch between the key character and characters in a corresponding search range in the lexicon entry. If the number of mismatches for all of the key characters in a search string is greater than (1+(the number of key characters in the search string/4)), then the lexicon entry is eliminated. Accordingly, the invention advantageously accomplishes lexicon reduction, thereby decreasing the time required to recognize a line of cursive text, without reducing accuracy.
摘要翻译：一种使用关键字符和神经网络进行词典缩减的方法，装置和制品，用于识别草书文本。识别草书图像的明确部分，称为“关键字符”。如果一行草图文本的一段是特定字符的置信度高于阈值，并且也足够高于相邻段的置信度，则该字符被指定为关键字符候选。然后使用几何信息对关键字候选进行筛选。通过筛选的关键角色候选人被指定为关键角色。采用两个阶段的词典缩小。词典缩减的第一阶段使用神经网络来估计草图文本行中的字符数的下限和上限。消除了界限外的字符总数的词典条目。对于词汇缩减的第二阶段，通过使用关键字符比较字符串与词典条目进一步减少词汇。对于字符串中的每个关键字符，确定在词典条目中的相应搜索范围中的关键字符和字符之间是否存在不匹配。如果搜索字符串中所有关键字符的匹配数量大于（1+（搜索字符串/ 4中的关键字符数）），则排除词典条目。因此，本发明有利地实现词典缩减，从而减少了识别草稿文本行所需的时间，而不降低精确度。

2. 发明授权

US06327386B1 Key character extraction and lexicon reduction for cursive text recognition 有权
标题翻译：草图文本识别的关键字符提取和词典缩减
公开(公告)号：US06327386B1
公开(公告)日：2001-12-04
申请号：US09635200
申请日：2000-08-09
申请人： Jianchang Mao , Matthias Zimmerman
发明人： Jianchang Mao , Matthias Zimmerman
IPC分类号： G06K962
CPC分类号： G06K9/00872
摘要： A method, apparatus, and article of manufacture employing lexicon reduction using key characters and a neural network, for recognizing a line of cursive text. Unambiguous parts of a cursive image, referred to as “key characters,” are identified. If the level of confidence that a segment of a line of cursive text is a particular character is higher than a threshold, and is also sufficiently higher than the level of confidence of neighboring segments, then the character is designated as a key character candidate. Key character candidates are then screened using geometric information. The key character candidates that pass the screening are designated key characters. Two-stages of lexicon reduction are employed. The first stage of lexicon reduction uses a neural network to estimate a lower bound and an upper bound of the number of characters in a line of cursive text. Lexicon entries having a total number of characters outside of the bounds are eliminated. For the second stage of lexicon reduction, the lexicon is fitter reduced by comparing character strings using the key characters, with lexicon entries. For each of the key characters in the character strings, it is determined whether there is a mismatch between the key character and characters in a corresponding search range in the lexicon entry. If the number of mismatches for all of the key characters in a search string is greater than (1+(the number of key characters in the search string/4)), then the lexicon entry is eliminated. Accordingly, the invention advantageously accomplishes lexicon reduction, thereby decreasing the time required to recognize a line of cursive text, without reducing accuracy.
摘要翻译：一种使用关键字符和神经网络来进行词典缩减的方法，装置和制品，用于识别草书文本。识别草书图像的明确部分，称为“关键字符”。如果一行草图文本的一段是特定字符的置信度高于阈值，并且也足够高于相邻段的置信度，则该字符被指定为关键字符候选。然后使用几何信息对关键字候选进行筛选。通过筛选的关键角色候选人被指定为关键角色。采用两个阶段的词典缩小。词典缩减的第一阶段使用神经网络来估计草图文本行中的字符数的下限和上限。消除了界限外的字符总数的词典条目。对于词典缩减的第二阶段，通过使用关键字符与词典条目比较字符串来缩小词典。对于字符串中的每个关键字符，确定在词典条目中的相应搜索范围中的关键字符和字符之间是否存在不匹配。如果搜索字符串中所有关键字符的匹配数量大于（1+（搜索字符串/ 4中的关键字符数）），则排除词典条目。因此，本发明有利地实现词典缩减，从而减少了识别草稿文本行所需的时间，而不降低精确度。

3. 发明授权

US06259812B1 Key character extraction and lexicon reduction cursive text recognition 失效
标题翻译：关键字提取和词典缩减草书文本识别
公开(公告)号：US06259812B1
公开(公告)日：2001-07-10
申请号：US09635201
申请日：2000-08-09
申请人： Jianchang Mao , Matthias Zimmerman
发明人： Jianchang Mao , Matthias Zimmerman
IPC分类号： G06K918
CPC分类号： G06K9/00872
摘要： A method, apparatus, and article of manufacture employing lexicon reduction using key characters and a neural network, for recognizing a line of cursive text. Unambiguous parts of a cursive image, referred to as “key characters,” are identified. If the level of confidence that a segment of a line of cursive text is a particular character is higher than a threshold, and is also sufficiently higher than the level of confidence of neighboring segments, then the character is designated as a key character candidate. Key character candidates are then screened using geometric information. The key character candidates that pass the screening are designated key characters. Two-stages of lexicon reduction are employed. The first stage of lexicon reduction uses a neural network to estimate a lower bound and an upper bound of the number of characters in a line of cursive text. Lexicon entries having a total number of characters outside of the bounds are eliminated. For the second stage of lexicon reduction, the lexicon is further reduced by comparing character strings using the key characters, with lexicon entries. For each of the key characters in the character strings, it is determined whether there is a mismatch between the key character and characters in a corresponding search range in the lexicon entry. If the number of mismatches for all of the key characters in a search string is greater than (1+(the number of key characters in the search string/4)), then the lexicon entry is eliminated. Accordingly, the invention advantageously accomplishes lexicon reduction, thereby decreasing the time required to recognize a line of cursive text, without reducing accuracy.
摘要翻译：一种使用关键字符和神经网络来进行词典缩减的方法，装置和制品，用于识别草书文本。识别草书图像的明确部分，称为“关键字符”。如果一行草图文本的一段是特定字符的置信度高于阈值，并且也足够高于相邻段的置信度，则该字符被指定为关键字符候选。然后使用几何信息筛选关键字候选人。通过筛选的关键角色候选人被指定为关键角色。采用两个阶段的词典缩小。词典缩减的第一阶段使用神经网络来估计草图文本行中的字符数的下限和上限。消除了界限外的字符总数的词典条目。对于词汇缩减的第二阶段，通过使用关键字符比较字符串与词典条目进一步减少词汇。对于字符串中的每个关键字符，确定在词典条目中的相应搜索范围中的关键字符和字符之间是否存在不匹配。如果搜索字符串中所有关键字符的匹配数量大于（1+（搜索字符串/ 4中的关键字符数）），则排除词典条目。因此，本发明有利地实现词典缩减，从而减少了识别草稿文本行所需的时间，而不降低精确度。

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