Handwritten character recognition is a very challenging task. The reason why it is more difficult than recognizing printed characters, the author believes, is mainly due to the following aspects:
First, the diversity of handwritten text styles is the main source of recognition difficulty. We know that a signature has legal effect, why? Because of its uniqueness, the characters written by different people will be different. Even if it is an imitation master, the imitated characters will be somewhat different, which causes the shape, size and style of the handwritten characters to be different, and there are many styles. , which adds great difficulty to OCR software recognition.
Secondly, the edges of handwritten characters are often unclear, fuzzy, and irregular. We know that most of the paper used for printing text is A4 paper. This kind of paper is generally thicker, basically it will not cause ink to spread, and the text is clear and neat. What about handwritten text? In the past, many of them used very thin paper such as letter paper, and what’s worse, some of them were written with pens. You must know that the ink from the pen is not always uniform, and often a large drop of ink will come out, causing "blurring" In this way, the edges of the written characters will be very unclear, and factors such as movement and shaking during handwriting will make the handwritten characters blurred and irregular, increasing the difficulty of recognition.
In addition, handwritten text has poor context relevance and strong randomness. Compared with printed text, handwritten text is more likely to have broken strokes and words. It is often less standardized, more random, and the continuity and coherence of the context may be poor. OCR software generally uses optical principles to identify In addition, when the recognized text is not clear enough, the "guess" algorithm will be called, and the target text needs to be "guessed" according to the meaning of the context. Guess" is wrong.
Also, there is a problem of similar shapes between some characters in handwritten text, such as the letter "O" and the number "0", or the confusion between the letter "l" and "1", which increases the difficulty of recognition.
Finally, there is relatively little training data for handwritten characters, and it is difficult to collect handwritten character data sets of sufficient scale and diversity. The scarcity of data has also become a bottleneck for handwritten character recognition algorithm training and performance improvement.
To sum up, handwritten character recognition is a challenging task that needs to be solved by comprehensively using various technologies and algorithms such as image processing, pattern recognition, and machine learning. Only through continuous optimization and improvement can more accurate and efficient handwritten character recognition be achieved. #Handwriting Font Recognition#