Abstract: An image matching system includes a non-transitory computer-readable medium and a processor. The non-transitory computer-readable medium is configured to store information of a plurality of images. The processor is configured to identify an object area in an original image that illustrates an object. The processor is configured to normalize the object area, resulting in a normalized image. The processor is configured to calculate a shape vector and a color vector from the normalized image. The processor is configured to calculate a match score using the shape vector and the color vector. The processor is configured to determine if the non-transitory computer-readable medium stores an identical match for the original image based on the match score.

Abstract: In one embodiment, a translator quality assurance system may rate a translation using a mapping chart. A data interface 150 may access a translation dictionary matching a source language to a target language. A processor 120 may map automatically a sentence pair having a source sentence 310 in a source language and a target sentence 320 in a target language at a linguistic level using the translation dictionary. A user output device 170 may display a translation quality report of the target sentence 320.

Abstract: One or more techniques and/or systems are disclosed for matching a target string to a known string. A target string is broken into one or more target terms, and the one or more target terms are matched to known terms in an index tree. The index tree comprises one or more known terms from a plurality of known strings, where the respective known terms in the index tree are associated with one or more known string IDs. A known term that is associated with a known string ID (in the index tree, and to which a target term is matched), is comprised in a known string, which corresponds to the known string ID. The target string can be matched to the known string using the known string's corresponding known string ID that is associated with a desired number of occurrences in the matching of the one or more target terms.

Abstract: In one embodiment, a translator quality assurance system may rate a translation using a mapping chart. A data interface 150 may access a translation dictionary matching a source language to a target language. A processor 120 may map automatically a sentence pair having a source sentence 310 in a source language and a target sentence 320 in a target language at a linguistic level using the translation dictionary. A user output device 170 may display a translation quality report of the target sentence 320.

Abstract: In one embodiment, a translator quality assurance system may rate a translation using a mapping chart. A data interface 150 may access a translation dictionary matching a source language to a target language. A processor 120 may map automatically a sentence pair having a source sentence 310 in a source language and a target sentence 320 in a target language at a linguistic level using the translation dictionary. A user output device 170 may display a translation quality report of the target sentence 320.

Abstract: Various embodiments provide a set of algorithms and scripts, e.g., SQL scripts, to perform automatic horizontal table and index partitioning that allows overlapping of data records, used in full-text search queries. In at least some embodiments, table creation and populating script is utilized to create a set of smaller tables partitioned by one column. In at least some embodiments, SELECT query modification script is provided to generate a query that works on the partitioned tables to achieve better performance. In at least some other embodiments, other query creation script is provided to modify the INSERT/UPDATE/DELETE queries for the original large table to automatically keep these queries synchronized with partitioned tables.

Abstract: In one embodiment, a translator quality assurance system may rate a translation using a mapping chart. A data interface 150 may access a translation dictionary matching a source language to a target language. A processor 120 may map automatically a sentence pair having a source sentence 310 in a source language and a target sentence 320 in a target language at a linguistic level using the translation dictionary. A user output device 170 may display a translation quality report of the target sentence 320.

Abstract: Various embodiments provide a set of algorithms and scripts, e.g., SQL scripts, to perform automatic horizontal table and index partitioning that allows overlapping of data records, used in full-text search queries. In at least some embodiments, table creation and populating script is utilized to create a set of smaller tables partitioned by one column. In at least some embodiments, SELECT query modification script is provided to generate a query that works on the partitioned tables to achieve better performance. In at least some other embodiments, other query creation script is provided to modify the INSERT/UPDATE/DELETE queries for the original large table to automatically keep these queries synchronized with partitioned tables.

Abstract: One or more techniques and/or systems are disclosed for matching a target string to a known string. A target string is broken into one or more target terms, and the one or more target terms are matched to known terms in an index tree. The index tree comprises one or more known terms from a plurality of known strings, where the respective known terms in the index tree are associated with one or more known string IDs. A known term that is associated with a known string ID (in the index tree, and to which a target term is matched), is comprised in a known string, which corresponds to the known string ID. The target string can be matched to the known string using the known string's corresponding known string ID that is associated with a desired number of occurrences in the matching of the one or more target terms.

Abstract: Various embodiments provide a translation memory system that utilizes sentence-level fuzzy matching and a scoring algorithm based on direct alignment. In one or more embodiments, a fuzzy match scoring formula includes use of an edit operation definition to define various deductions that are computed as part of an overall score, an overall scoring algorithm, and word-level scoring and partial match definitions. A direct alignment algorithm finds a computed alignment between two sentences using a pair-wise difference matrix associated with a primary sentence and a comparison sentence. An overall algorithm identifies editing operations such as replacements, position swaps and adjustments for a final score calculation. Once final scores are calculated between the primary sentence and multiple comparison sentences, a primary/comparison sentence pair can be selected, based on the score, to serve as a basis for translating the primary sentence.

Abstract: Input text may be broken into sentence, or other types of segments, by first detecting exceptions in the input text, and then detecting break positions. Given a segment breaking scheme that comprises a set of break rules and a set of exceptions, a regular expression is created that represents the break rules, and another regular expression is created that represents the exceptions. The input text is analyzed to identify strings that match any exception, and the matching strings are substituted with placeholders that are not likely to occur naturally in the input. The resulting text, with substitutions, is then evaluated to find the positions in the text that match the break rules. Those positions are declared to be segment breaks, and the placeholders are then replaced with the original strings. The result is the original text, with breaks assigned to the appropriate positions in the text.

Abstract: Input text may be broken into sentence, or other types of segments, by first detecting exceptions in the input text, and then detecting break positions. Given a segment breaking scheme that comprises a set of break rules and a set of exceptions, a regular expression is created that represents the break rules, and another regular expression is created that represents the exceptions. The input text is analyzed to identify strings that match any exception, and the matching strings are substituted with placeholders that are not likely to occur naturally in the input. The resulting text, with substitutions, is then evaluated to find the positions in the text that match the break rules. Those positions are declared to be segment breaks, and the placeholders are then replaced with the original strings. The result is the original text, with breaks assigned to the appropriate positions in the text.