OCR technology, what kind of technology is it?

In our daily life and work, we will inevitably encounter some problems, such as their hard work to write the information, it is difficult to print out but found that the source file is lost; collected some business cards, but it is very troublesome to enter the information one by one.

So, is there any technology that can help us solve these problems?

Yes, that is OCR text to recognize information technology.

OCR English full name is Optical Character Recognition, online pdf conversion is called Optical Character Image Recognition. It is the use of information optical system technology and computer network technology to print or write on paper text data read out, and converted to have a through the development of computers can continue to accept, people can fully understand the format.

For example, a cell phone application can help scan business cards and ID cards and identify the information inside; cars don't need to be manually registered to enter a parking lot or a toll booth, they all use license plate recognition technology When we read a book, we see a problem that we don't understand. We take out a cell phone, scan it, and an app helps you find the answer on the Internet, and so on. All of this relies on OCR technology.

The purpose of the OCR recognition system is very simple, is to transform the image, so that the graphics in the image continue to be saved, the data in the form and the characters in the image are turned into computer characters,merge pdf rearrange pages which can reduce the storage of image data, so that the recognized characters can be reused and analyzed, which will greatly save the keyboard input of manpower and time to improve office automation, to achieve a true end-to-end business process automation The following is a summary of the process.

1. Image Preprocessing

Because the thickness, smoothness and printing quality of paper can cause text distortion, resulting in broken strokes, sticking and spots and other interference, so before the text recognition,pdf split and merge download online the text image with noise should be processed.

Because this processing work is before the character recognition, it is called preprocessing, generally including grayscaling, binarization, skew detection and correction, line and word segmentation, smoothing, standardization and so on.

Traditional OCR is based on digital image data processing and traditional machines can learn and other methods to analyze and process the image problem and feature extraction. The commonly used binarization processing is beneficial to enhance the text information of a simple scene, but for a complex social background binarization has little effect.

Due to the rapid development of deep learning, CNN-based neural networks have been widely used as a feature extraction method. As cellular neural networks have a strong learning ability, they can enhance the robustness of feature extraction with a large amount of data, and perform well in problems such as image blurring, distortion, distortions, complex backgrounds and light blurring.

2.Text Detection

CTPN is one of the most widely used text detection models.

Its basic assumption is that individual characters are easier to detect compared to more heterogeneous lines of text, so the R-CNN-like detection of individual characters is performed first.

After that, a bidirectional LSTM is added to the detection network to sequence the detection results and provide contextual features of the text so that multiple characters can be merged to get lines of text.

There are some problematic studies that introduce an attention mechanism, such as the following model can be used to risk assess the weight data of the image using the Dense Attention model. Such a favorable separation of the foreground image and background image, for the network text content compared to the background image between has its own higher attention, so that the results of the detection work more accurate.

3.Text Recognition

Visual Attention Model (CNN+LSTM+Attention Technology), the model firstly uses the sliding window CNN (Convolutional Neural Network) method for image feature extraction on the image, and then stacks an LSTM (Long Short-Term Memory networks) on top of the CNN for sequence feature extraction, and finally, the attention model is used as a decoder to output the final text sequence.

Although OCR based on deep learning performs better compared to traditional methods, deep learning techniques still need to be specialized in the field of OCR, on the other hand, as the driving force of deep learning, data plays a vital role, so collecting a wide range of high-quality data is also one of the most important initiatives for the performance of OCR at this stage.

Because of the use of OCR technology, it quickly and efficiently realize the information collection and entry, no longer need to waste manpower to enter the registration, also do not need to spend a lot of material resources. It saves time and cost, greatly improve the efficiency of work at the same time, also subverted the traditional mode of work, for all walks of life to information technology to contribute to the community.