diff --git a/test/DefaultLabelTypes_3.xml b/test/DefaultLabelTypes_3.xml index 97bfb3d..1ba7532 100644 --- a/test/DefaultLabelTypes_3.xml +++ b/test/DefaultLabelTypes_3.xml @@ -193,7 +193,7 @@ Free / paid for Text represents natural language. Examples: -A news artcile +A news article Related: @@ -226,7 +226,7 @@ Online handwriting - General domain, research field or specific processing strategy of a workflow activty. + General domain, research field or specific processing strategy of a workflow activity. Examples: An activity for automated number plate recognition could be labelled with "OCR" domain. @@ -333,7 +333,7 @@ OCR Examples: Stock exchange data in a newspaper, -Filled in questionaire form +Filled in questionnaire form Related: OCR @@ -419,7 +419,7 @@ Text recognition (Visual Computing) Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation. Examples: -Digitial assistents (e.g. in smartphones) +Digital assistants (e.g. in smartphones) Related: OCR @@ -457,7 +457,7 @@ Examples: A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. Related: -Named entitiy recognition, +Named entity recognition, Tokenisation (as part of Data creation / transformation) @@ -682,7 +682,7 @@ Part-of-speech tagging Examples: Part-of-speech tagging, -Named entitiy tagging, +Named entity tagging, Page layout annotation (regions etc.) Related: @@ -795,7 +795,7 @@ Licence Experimental, in development, prototype - Production-strengh method / system that is reliable, tested, and robust + Production-strength method / system that is reliable, tested, and robust @@ -819,7 +819,7 @@ Whiteboard writing Related: Physical production method - The data was orignially produced on paper + The data was originally produced on paper Example: Printed magazine @@ -993,7 +993,7 @@ Source medium - Source / target content. What is the intersting bit in the data at hand. + Source / target content. What is the interesting bit in the data at hand. @@ -1096,7 +1096,7 @@ Book Conditions introduced during the production of the medium / object - Document-related charactersitics + Document-related characteristics Paper clippings pasted onto a page @@ -1110,7 +1110,7 @@ Book The content of a page reaches very close to the page border or even touches it - The contrast bwtween the paper and the page content is very low + The contrast between the paper and the page content is very low Dot-based halftoning printing technique was used (to emulate more colours / grey tones) @@ -1283,8 +1283,8 @@ Book Foreign objects visible - - Part of preceeding or succeeding object included (e.g. other page) + + Part of preceding or succeeding object included (e.g. other page) Medium structure visible (e.g. book cover) diff --git a/xsd_schema/OCR-D_GT_schema.xsd b/xsd_schema/OCR-D_GT_schema.xsd index bb18471..5d372a0 100644 --- a/xsd_schema/OCR-D_GT_schema.xsd +++ b/xsd_schema/OCR-D_GT_schema.xsd @@ -264,7 +264,7 @@ Text represents natural language. Examples: - A news artcile + A news article Related: @@ -303,7 +303,7 @@ - General domain, research field or specific processing strategy of a workflow activty. + General domain, research field or specific processing strategy of a workflow activity. Examples: An activity for automated number plate recognition could be labelled with "OCR" domain. @@ -436,7 +436,7 @@ Examples: Stock exchange data in a newspaper, - Filled in questionaire form + Filled in questionnaire form Related: OCR @@ -537,7 +537,7 @@ Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation. Examples: - Digitial assistents (e.g. in smartphones) + Digital assistants (e.g. in smartphones) Related: OCR @@ -584,7 +584,7 @@ A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. Related: - Named entitiy recognition, + Named entity recognition, Tokenisation (as part of Data creation / transformation) @@ -851,7 +851,7 @@ Examples: Part-of-speech tagging, - Named entitiy tagging, + Named entity tagging, Page layout annotation (regions etc.) Related: @@ -989,7 +989,7 @@ - Production-strengh method / system that is reliable, tested, and robust + Production-strength method / system that is reliable, tested, and robust @@ -1023,7 +1023,7 @@ - The data was orignially produced on paper + The data was originally produced on paper Example: Printed magazine @@ -1238,7 +1238,7 @@ - Source / target content. What is the intersting bit in the data at hand. + Source / target content. What is the interesting bit in the data at hand. @@ -1367,7 +1367,7 @@ - Document-related charactersitics + Document-related characteristics @@ -1677,9 +1677,9 @@ Foreign objects visible - + - Part of preceeding or succeeding object included (e.g. other page) + Part of preceding or succeeding object included (e.g. other page)