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| '''Type''': An interesting and practical bachelor thesis. A basic understanding of Machine Learning''' is required'''; knowledge of Deep Learning is desirable. The preferred programming language is Python. | '''Type''': An interesting and practical project. A basic understanding of Machine Learning''' is required'''; knowledge of Deep Learning is desirable. The preferred programming language is Python. |
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| '''Background info''': TODO | '''Background info''': Words in text documents can be hyphenated (especially frequent in layouts with multiple columns), in which case they appear broken in two parts at different positions in the text and need to be merged to single words when text from the documents is extracted (e.g., for indexing and searching the documents). The hyphen between two parts of a word can be mandatory even after merging when the word is a compound of two words and the hyphen is used to combine them. Here is an example: |
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| '''Goal''': Merging hyphenated words by using machine learning techniques; taking into account that a word can be a compound word, in which case the hyphen between the two parts of the word needs to be retained on merging the parts. | TODO: Example. |
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| ''Challenge 1'': TODO | Note the parts highlighted in blue. The word ''high-quality'' is a compound word and thus, the hyphen between ''high'' and ''quality'' needs to be retained after merging the parts. In contrast, the word ''benchmark'' is not a compound word and thus, the hyphen needs to be removed on merging the parts.<<BR>><<BR>> |
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| ''Challenge 2'': TODO | '''Goal''': Merging hyphenated words by using machine learning techniques, by taking into account that a word can be a compound word, in which case the hyphen between the two parts of the word needs to be retained on merging the parts. |
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| '''Subgoal 1''': TODO | ''Challenge 1'': The decision, whether or not the hyphen between the parts of a hyphenated word needs to be retained (because of a compound word, see above). |
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| '''Subgoal 2''': TODO | '''Subgoal 1''': Design, implement and train a machine learning model for merging hyphenated words. This includes to create large and meaningful training data for the model. '''Subgoal 2''': A thorough evaluation of your approach. |
Accurate Word Extraction from Documents with Complex Layouts
Type: An interesting and practical project. A basic understanding of Machine Learning is required; knowledge of Deep Learning is desirable. The preferred programming language is Python.
Background info: Words in text documents can be hyphenated (especially frequent in layouts with multiple columns), in which case they appear broken in two parts at different positions in the text and need to be merged to single words when text from the documents is extracted (e.g., for indexing and searching the documents). The hyphen between two parts of a word can be mandatory even after merging when the word is a compound of two words and the hyphen is used to combine them. Here is an example:
TODO: Example.
Note the parts highlighted in blue. The word high-quality is a compound word and thus, the hyphen between high and quality needs to be retained after merging the parts. In contrast, the word benchmark is not a compound word and thus, the hyphen needs to be removed on merging the parts.
Goal: Merging hyphenated words by using machine learning techniques, by taking into account that a word can be a compound word, in which case the hyphen between the two parts of the word needs to be retained on merging the parts.
Challenge 1: The decision, whether or not the hyphen between the parts of a hyphenated word needs to be retained (because of a compound word, see above).
Subgoal 1: Design, implement and train a machine learning model for merging hyphenated words. This includes to create large and meaningful training data for the model.
Subgoal 2: A thorough evaluation of your approach.
Supervision by Claudius Korzen.