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)