Mạng Nơ Ron nhân tạo trong nhận dạng văn bản và ứng dụng vào hệ thống nhận dạng chữ Việt in - Pdf 25



MỤC LỤC

MỞ ĐẦU
6
CHƢƠNG I TỔNG QUAN VỀ MẠNG NƠ -RON NHÂN TẠO VÀ HỆ
THỐNG NHẬN DẠNG VĂN BẢN
8
1.1. Giới thiệu về mạng nơ ron
8
1.1.1. Mô hình mạng nơ-ron nhân tạo
8
1.1.2. Ƣu và nhƣợc điểm của mạng nơ-ron
12
1.2. Phân loại mạng nơ ron
14
1.3. Hệ thống nhận dạng văn bản
17
1.3.1. Các hoạt động tiền xử lý
20
1.3.2. Phân tích trang văn bản
22
1.3.3. Trích chọn đặc trƣng
27
1.3.4. Học và nhận dạng
33
1.3.5. Hậu xử lý
34
3.2.3. Hậu xử lý
85
3.3. Kết quả đạt đƣợc
85
3.3.1. Kết quả nhận dạng các ký tự riêng lẻ
86
3.3.2. Kết quả nhận dạng văn bản
86
KẾT LUẬN
89
TÀI LIỆU THAM KHẢO
92 - 6 -
MỞ ĐẦU

Mạng nơ ron nhân tạo ngày nay đang là một lĩnh vực nghiên cứu nóng
hổi, thu hút sự quan tâm đặc biệt của các nhà khoa học trong nhiều lĩnh vực. Đó
là nhờ những thành công rực rỡ cả về mặt lý thuyết và ứng dụng của nó. Phạm
vi áp dụng của mạng nơ ron rất rộng lớn: trong lĩnh vực xử lý, điều khiển nhƣ
xử lý tín hiệu, khử nhiễu, phân lớp, nhận dạng, dự báo. Ngoài ra mạng nơ ron
còn đƣợc ứng dụng trong các lĩnh vực toán học, y học, kinh doanh, tài chính,
nghệ thuật. Mạng nơ ron đang ngày càng trở thành một hƣớng nghiên cứu, một
giải pháp ngày càng hứa hẹn.
Một trong những ứng dụng quan trọng của mạng nơ ron là nhận dạng và
phân loại mẫu. Khả năng học và thích ứng của chúng đã làm cho chúng là lựa
chọn hàng đầu đối với nhiệm vụ so sánh các tập dữ liệu hoặc trích chọn các mẫu
thích hợp từ các dữ liệu phức hợp. Nhận dạng mẫu trong mạng nơ ron là một
lĩnh vực rất rộng, nhƣng phổ biến nhất vẫn là nhận dạng chữ in và chữ viết tay.

Chƣơng ba trình bày về hệ thống nhận dạng chữ Việt in sử dụng mạng
nơ ron.

- 8 -
CHƢƠNG I
TỔNG QUAN VỀ MẠNG NƠ RON NHÂN TẠO VÀ HỆ THỐNG
NHẬN DẠNG KÝ TỰ 1.1. Giới thiệu về mạng nơ-ron
Mạng nơ-ron nhân tạo đƣợc xây dựng từ những năm 1940, nhằm mô
phỏng một số chức năng bộ não của con ngƣời. Nếu nhìn não từ góc độ tính
toán, chúng ta dễ thấy rằng cách thức tính toán của não khác xa với tính toán
theo thuật toán và chƣơng trình truyền thống. Sự khác biệt thể hiện ở hai điểm:
+ Quá trình tính toán đƣợc tiến hành song song và phân tán trên nhiều nơ
ron gần nhƣ đồng thời
+ Tính toán thực chất là quá trình học chứ không phải theo một sơ đồ định
sẵn từ trƣớc.
Mạng nơ-ron nhân tạo đƣợc thiết kế tƣơng tự nhƣ nơ-ron sinh học, sẽ có
khả năng giải quyết hàng loạt các bài toán mà sự suy luận tƣơng đối “mờ”,
không đầy đủ dữ liệu, cần có quá trình “học” từ các ví dụ.

1.1.1. Mô hình mạng nơ-ron nhân tạo
1.1.1.1. Nơ-ron sinh học
Bộ não ngƣời có khoảng 10
11
tế bào thần kinh kết nối với mật độ cao gọi
là các nơ-ron (một nơ ron có khoảng 10
4
liên kết). Có nhiều loại nơ-ron khác

(i) Dạng tuyến tính:



N
1j
jiji
pwn

(1.1)
(ii) Dạng toàn phƣơng:

- 10 -



N
1j
2
jiji
pwn

(1.2)
(iii) Dạng mặt cầu:
 




N

Hình 1.3. Mô hình một nơ ron
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

Trích đoạn Wpk = (Σ tqpq T Đánh giá về mạng Adaline Chuyển về ảnh đen trắng Hiệu chỉnh độ lệch trang văn bản Tách ký tự khỏi từ
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