In this paper, we propose a computer-assisted transcription system of old registers, handwritten in Arabic from
the 19th century onwards, held in the National Archives of Tunisia (NAT). The proposed system assists the
human supervisor to complete the transcription task as efficiently as possible. This assistance is given at all
different recognition levels. Our system addresses different approaches for transcription of document images. It
also implements an alignment method to find mappings between word images of a handwritten document and
their respective words in its given transcription.
Recently, we have investigated the use of Arabic linguistic knowledge to improve the recognition of wide Arabic word lexicon. A neural-linguistic approach was proposed to mainly deal with canonical vocabulary of decomposable words derived from tri-consonant healthy roots. The basic idea is to factorize words by their roots and schemes. In this direction, we conceived two neural networks TNN_R and TNN_S to respectively recognize roots and schemes from structural primitives of words. The proposal approach achieved promising results. In this paper, we will focus on how to reach better results in terms of accuracy and recognition rate. Current improvements concern especially the training stage. It is about 1) to benefit from word letters order 2) to consider "sisters letters" (letters having same features), 3) to supervise networks behaviors, 4) to split up neurons to save letter occurrences and 5) to solve observed ambiguities. Considering theses improvements, experiments carried on 1500 sized vocabulary show a significant enhancement: TNN_R (resp. TNN_S) top4 has gone up from 77% to 85.8% (resp. from 65% to 97.9%). Enlarging the vocabulary from 1000 to 1700, adding 100 words each time, again confirmed the results without altering the networks stability.
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