Пушников А. А., ПС-569
Тезисы доклада на научно-практическую конференцию «Современные методы и средства автоматического управления техническими объектами»
On-line распознавание рукописных символов
В настоящее время, в связи с развитием миниатюрных компьютеров, таких как PDA, TabletPC, стоит проблема безклавиатурного ввода данных. Одним из альтернативных средств ввода является сенсорная панель. Был исследован способ метод распознавания рукописных символов с помощью искусственной нейронной сети.
Задача on-line распознавания состоит из трех основных этапов:
На этапе предварительной обработки данных выбираем основные признаки и преобразуем их в удобную для классификации форму, например вектор в каком-либо пространстве. В задаче on-line распознавания известен путь пера, в виде последовательности точек. Для упрощения классификатора, уменьшаем количество точек до некоторого фиксированного числа n. Такая выборка делает расстояния между точками практически одинаковыми, что позволяет не учитывать при распознавании размер символа. Для инвариантности символа относительно его положения на изображении, преобразуем входную последовательность в вектор синусов и косинусов углов между осями координат и прямой, соединяющей две соседние точки. В результате получаем вектор размерностью 2*(n -1).
На этапе классификации разделяем пространство входных векторов на отдельные области, в качестве классификатора используем нейронная сеть. Была исследована двухслойная нейронная сеть обратного распространения с логарифмической сигмоидной функцией активации нейронов. Число входов – 2*(n -1), число выходов равно числу символов. Такая нейронная сеть позволит получить на выходе оценку совпадения исследуемого образа с исходными.
При исследовании выбиралось: n=10, количество входных нейронов – 18, количество исследуемых символов – 4, количество выходных нейронов – 4. Схема нейронной сети представлена на рис. 1. График тренировки нейронной сети представлен на рис. 2. Изображения обучающих символов представлены на рис. 3. Изображение тестового символа представлено на рис. 4. В таблице 1 представлены выходы нейронной сети.
Рисунок 1 - Схема нейронной сети
Рисунок 2 - График тренировки нейронной сети
Рисунок 3 - Изображения обучающих символов. Линией обозначены выбранные точки, окружностями - введенные
Рисунок 4 - Изображение тестового символа. Линией обозначены выбранные точки, окружностями – введенные
Таблица 1. Выход нейронной сети для тестового символа.
№ выхода нейронной сети | Выход нейронной сети | Название обучающего символа, соответствующего данному выходу |
1 | 0.9996 | Символ «а» |
2 | 0.0000 | Символ «б» |
3 | 0.0000 | Символ «в» |
4 | 0.0001 | Символ «г» |
На этапе принятия решения делаем вывод, на какой из обучающих символов более похож тестовый символ. Для этого выбираем символов, которому соответствует наибольшее значение выхода нейронной сети. Если же максимальное значение не превысит некоторое пороговое значение или несколько выходов будут иметь близкие значения, то делаем вывод, что введен неизвестный символ.
Исследованная модель уверенно распознает рукописные символы и позволяет сделать вывод о том является ли исследуемый символ одним из известных ранее.
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