Handwritten Historical Document Analysis, Recognition, and Retrieval: State of the Art

Handwritten Historical Document Analysis, Recognition, And Retrieval - State Of The Art And Future Trends (Series In Machine Perception And Artificial Intelligence Book 89)

by Renee Greene

5 out of 5

Language	:	English
File size	:	17059 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	270 pages
Screen Reader	:	Supported
Paperback	:	30 pages
Item Weight	:	4.5 ounces
Dimensions	:	8.5 x 0.07 x 11 inches

FREE

Handwritten historical documents are a valuable source of information, but they can be difficult to read and interpret. In recent years, there has been growing interest in the development of automated systems for the analysis, recognition, and retrieval of handwritten historical documents.

The analysis of handwritten historical documents typically involves the following steps:

1. Preprocessing: This step involves cleaning up the document image, removing noise, and segmenting the document into lines and words.
2. Feature extraction: This step involves extracting features from the document image that can be used to identify the characters and words in the document.
3. Classification: This step involves using a machine learning algorithm to classify the characters and words in the document.
4. Recognition: This step involves converting the classified characters and words into a machine-readable format.

The recognition of handwritten historical documents is a challenging task due to the variability of handwriting, the presence of noise and artifacts in the document image, and the lack of labeled training data.

In recent years, there has been significant progress in the development of machine learning algorithms for the recognition of handwritten historical documents. These algorithms have been able to achieve high levels of accuracy on a variety of historical document datasets.

The retrieval of handwritten historical documents is a challenging task due to the lack of structured metadata and the difficulty of searching through large collections of documents.

In recent years, there has been growing interest in the development of automated systems for the retrieval of handwritten historical documents. These systems have been able to achieve high levels of accuracy on a variety of historical document retrieval tasks.

The analysis, recognition, and retrieval of handwritten historical documents is a challenging task, but it is a task that is becoming increasingly important as more and more historical documents are digitized.

In recent years, there has been significant progress in the development of automated systems for the analysis, recognition, and retrieval of handwritten historical documents. These systems have the potential to make a significant contribution to the preservation and accessibility of our historical heritage.

Handwritten Historical Document Analysis, Recognition, And Retrieval - State Of The Art And Future Trends (Series In Machine Perception And Artificial Intelligence Book 89)

by Renee Greene

5 out of 5

Language	:	English
File size	:	17059 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	270 pages
Screen Reader	:	Supported
Paperback	:	30 pages
Item Weight	:	4.5 ounces
Dimensions	:	8.5 x 0.07 x 11 inches

FREE