fa تشخیص دست‌نوشتۀ‌ برخط فارسی با استفاده از مدل زبانی و کاهش قوانین نگارش کاربر مقالات پردازش متن Paper پژوهشي Research <p dir="RTL" style="text-align: justify;"><strong><span style="font-family:b nazanin;">پیوسته‌بودن کلمات فارسی و وجود تنوع بسیار زیاد رسم‌الخط این زبان و همچنین شکل‌های متنوع حروف فارسی بسته به محل قرارگیری‌شان در کلمه، تشخیص دست‌نوشته‌های فارسی را به چالش کشانده‌اند. مهم‌ترین اشکال در اغلب روش‌های بازشناسی بی‌توجهی به بافت جمله است که باعث می‌شود در مواردی که کلمه ورودی اشتباه بازشناسی می‌شود، واژه‌ای با ظاهر درست در جمله‌ای نابه‌جا به کار رود. طراحی مدلی که بتواند بافت جمله را به‌خوبی تحلیل کند، مستلزم در‌اختیار‌داشتن منابع زبانی حجیمی است که نمایندۀ خوبی از زبان مورد بازشناسی باشند. در این مقاله روش جدیدی برای بازشناسی کلمات برخط فارسی ارائه شده است که با استفاده از بافت جمله سعی در بهبود بازشناسی دارد. فرآیند بازشناسی معرفی‌شده در این نوشتار به این صورت است که ابتدا علائم و بدنه زیرکلمات دست‌نوشته ورودی تفکیک شده و بدنه هر زیرکلمه و علائم آن مشخص می‌شود؛ سپس علائم زیرکلمات تشخیص داده‌شده و بر اساس آن مجموعه‌ای از واژگان به‌عنوان فرضیه در نظر گرفته می‌شوند؛ به هر فرضیه بر اساس میزان شباهت آن به دست‌نوشته ورودی امتیازی تعلق می‌گیرد و بر اساس امتیاز حاصله محتمل‌ترین فرضیات مشخص می‌شوند. سپس این رویه توسط مدل زبانی برای یافتن فرضیات محتمل‌تر، هدایت می‌شود. نتایج آزمایش‌های به‌عمل‌آمده نشان می‌دهد که کاهش قابل توجهی در نرخ خطای بازشناسی کلمات حاصل شده و کاربر در نگارش ملزم به رعایت محدودیت‌های کمتری است. از طرفی روش پیشنهادی می‌تواند نسبت به روش‌های قبلی با در‌اختیار‌داشتن یک پایگاه داده دست‌نویس محدود، صحت مطلوب‌تری ارائه کند. با به‌کارگیری روش ارائه‌شده، دقت بازشناسی در مرحلۀ‌ اولیه در سطح حروف 9/95% و پس از بازشناسی به‌کمک مدل زبانی دقت بازشناسی به 3/99% ارتقا یافت.</span></strong> <strong><span style="font-family:b nazanin;">برای بهبود عملکرد الگوریتم، استفاده از الگوریتم یادگیری تقویتی برای تطبیق پذیری الگوریتم با نویسنده به‌عنوان کار آینده پیشنهاد می‌شود.</span></strong><br> &nbsp;</p> <p style="margin-left: 28.35pt; text-align: justify;"><strong>The Joint-up, cursive form of Persian words and immense variety of its scripts, also different figures of Persian letters depending on their sitting positions in the words, have turned the Persian handwritings recognition to an intense challenge. The major obstacle of the most often recognition ways, is their inattention to sentence contexture which causes utilizing of a word with correct appearance within an incorrect sentence, when an input word is misrecognized. Sketching a solution that provides suitable analysis of sentence contexture, requires huge linguistic resources to take place as a fine representative for the chosen language to be recognized. In this article, a new method for online recognition of Persian words is presented which tries to improve recognition process by using the term contexture. </strong><strong>In this article, the vocabularies collection of Persian language is divided into two groups. The first category is the vocabulary with all of their sub-words being supported by the database of handwritten subclasses, while these vocabulary form 68.2% of the total vocabulary, and the assumptions being scored at the recognition stage, are members of these vocabularies. The second category is the vocabulary that is not supported by the database. Obviously, if the recognition system does not support this vocabulary, it cannot recognize more than 30 percentages of the language&#39;s words. At the recognition stage, the symptoms are detected and a symptom tag is produced. Also, at this stage, using the same label, the vocabulary is also selected as the sign with the input word. (These vocabularies are chosen from those were not supported at the recognition stage). Scoring for hypotheses was done by combining recognition scores and linguistic models. The certain fact in this section is that it is impossible to calculate recognition scores due to the absence of hypothetical subheadings. Therefore, the vocabulary score being recognized in the previous steps, is used. According to the studies, it was concluded that if the word is equivalent to a member&#39;s input from a supported vocabulary, even if the result of the recognition is incorrect, in most cases the correct term is in the first four hypotheses. Usually, scores of the first few hypotheses are close to each other, and the other assumptions are far from the correct hypothesis. Since the system operates online, unnecessary computations should be avoided. Therefore, if the number of hypotheses in the recognition section are more than four hypotheses, only the first four hypotheses are calculated for the language model. To calculate the recognition score for new hypotheses, if there are fewer than four hypotheses in the recognition section, the lowest hypothesis score and otherwise the hypothesis score are considered for the recognition score of the new hypotheses. Then, as with previous assumptions, for the new hypotheses, the linguistic score is calculated, and then the final score is obtained for each hypothesis. Finally, the assumption with the highest score is considered as the system output, and the rest of the assumptions are displayed in the output to the user. Experiments show that even in the event of a mistake, the correct word is often presented as a second hypothesis in most cases, and in some cases as a third hypothesis. </strong><strong>Also, to reduce the limits and rules that gainers compel to submit. The method demonstrated in this article includes the symptoms and morphemes framework of input handwritten are segregated and the framework of each morpheme with its symptoms is specified at first, then the symptoms of morphemes are specified and based on them a collection of words is being considered as a hypothesis. Each hypothesis is given a score by measuring the similarity to input handwritten and according to taken scores, the likely hypotheses are indicated. Then, this procedure is led to achieve hypotheses more likely by lingual models. To totalize the scores of a hypothesis, for the differences in scale of taken scores, a method of score normalization is being offered. The results demonstrate that by utilizing of a language model with an online system of handwriting recognition, a significant reduction of words recognition error rate is being achieved. In addition to error rate reduction, by taking advantages of this language model, a technique is being offered that can handle the Persian vocabulary recognition entirely. By availing the offered manner, the recognition precision at initial stage of letters level up to 95.9% and so the language model recognition up to 99.3% improved. So, using huge linguistic resources for Persian language and utilizing a language model, can improve the accuracy of recognition. For further work, reinforcement learning algorithm is suggested to adapt the algorithm for users.</strong><br> &nbsp;</p> بازشناسی برخط, دست‌نوشته فارسی, نزدیک‌ترین همسایه, مدل زبانی, محدودیت کاربر Online Recognition, Persian Handwriting, k-nearest Neighbor, Language Model, User Limitation 3 24 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-116-2&slc_lang=fa&sid=1 Salman Maskanati سلمان مسکنتی Maskanati@gmail.com 10031947532846007120 10031947532846007120 No persian Gulf University دانشگاه خلیج فارس بوشهر Ahmad Keshavarz احمد کشاورز a.keshavarz@pgu.ac.ir 10031947532846007121 10031947532846007121 Yes Persian Gulf University دانشگاه خلیج فارس بوشهر