Brugia Malayi - Clark Science Center - Smith College

Handwriting Recognition in Classical Syriac
Cordelia Nowak
Much of human history and literature is preserved only in handwritten texts; these texts date back thousands of years and are
written in languages which few or no modern people understand. Collecting knowledge in easily accessible databases which can
be studied at researchers’ leisure is vastly important for academics in many different fields of the humanities. There is a wealth of
information and literature in libraries written in Classical Syriac, an ancient dialect of Middle Arameic. Handwriting recognition
is a difficult endeavor for a computer scientist: what a human mind can do in a second is enormously difficult for a computer.
Programs built to recognize handwriting must be able to accommodate for human error and a thousand variations in size and style
from writer to writer and even within the same text.
Our goal in this project was to get the program to be able to recognize individual characters from a Syriac document. In order
for the program to understand a handwritten word, it was necessary to use a classifier which would determine whether or not a
letter is in a particular word. The classifier which we chose is a Support Vector Machine which, when given positive and negative
examples of a certain letter, can be trained to identify whether or not there is a patch on the word which is a positive match for the
letter in question. By going through the alphabet and performing the test to identify whether or not the letter is in the word, the
program can eventually recreate the word and store it as a digital book.
Our work utilized scans from a Classical Syriac book of Genesis. Initially, when provided with a correct transcript of a section
of the book of Genesis containing roughly 1800 words, the program was able to superimpose and alter to fit a model (known as
a PSM or Part Structured Model) onto the text. The PSM is a general model for what a letter should look like composed of nodes
bound together by stretchable pointers so that the model can accurately fit over a letter. Once the program reached this level of
functionality, it became necessary to train the program to work without a transcript provided. At this point we trained an SVM
for every letter of the twenty-two in the Syriac alphabet. For each letter, we compiled a list of instances of that letter which were
labeled as positive examples and a list of all the other possible letters which were labeled as negative examples. Once training was
complete, the SVM was able to accurately predict what a letter was a large portion of the time, although some letters are still more
reliably accurate than other. Most are well over 90% accurate.
Although the project is by no means finished, we now have the correctly formatted data necessary to classify the characters
and we have a Support Vector Machine that is quite capable of forming the backbone of the program. This is a big improvement
where we had very little done with the text other than some manually completed transcripts and a file breaking the text into lines
and words. With relatively little work, we can soon have a fully functioning program capable of reliably recognizing Syriac text.
This program is well on its way towards being a serious asset for Syriac scholars. (Supported by the Schultz Foundation)
Adviser: Nicolas Howe
2012
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