Comparison for Handwritten Character Recognition and Handwritten Text Recognition and Tesseract Tool on IJAZAh�s Handwriting

Handwriting is a form of being able to recognize various types of
writing in various existing fonts. Unlike consistent computer letters, each human
handwriting is unique in its form and consistency. These problems can be found
in a document where the data is in the form of handwriting. Segmentation of the
data location will use a run length smoothing algorithm with points as segmentation
features. The Handwriting Text Recognition (HTR) technique requires segmented
data intowords. The HandwritingCharacter Recognition (HCR) technique
requires segmented data into various characters. The process of this HCR technique
uses the LeNet5 model using the EMNIST dataset. HTR uses the tesseract
tool and a convolutional iterative neural network using the IAM database. Experiment
on 10 samples of scan images, segmentation obtained an average accuracy
of 95.6%. The HCR technique failed in the letter segmentation process in cursive
handwriting. The easiest technique to use is the HTR with the helps of tesseract
tool, tesseract tool also has a good performance. Tesseract managed to get word
accuracy above 70% tested on 5 scan samples, 15 data fields.