Optical character recognition technology: principles and applications

Optical character recognition (OCR) is one of the important technologies for digital documents. It uses computer vision to detect and read text in images, combined with natural language processing algorithms to decipher and understand what the document conveys. This article will introduce the principles and applications of OCR technology in detail.

Optical character recognition technology implementation method

Machine learning-based method

Although based on Machine learning methods are fast to develop, but take much longer to run, and their accuracy and inference speed are easily surpassed by deep learning algorithms.

The optical character recognition method is pre-processed, cleaned and noise removed, and then the document is binarized for contour detection to aid in row and column detection.

Finally, characters are extracted, segmented and recognized through various machine learning algorithms such as K-nearest neighbor and support vector machine algorithms. Although it works well on simple OCR data sets, it may fail when processing complex data sets.

Deep learning-based method

This method can effectively extract a large number of features, combining vision and NLP-based algorithms for text recognition and Missions such as detection were particularly successful. Furthermore, this approach provides an end-to-end detection pipeline, liberating it from lengthy preprocessing steps.

Typically, optical character recognition (OCR) methods include vision-based methods for extracting text regions and predicting their bounding box coordinates. The bounding box data and image features are then passed to a language processing algorithm, which uses RNN, LSTM and Transformer to decode the feature-based information into text data.

Deep learning-based optical character recognition (OCR) has two stages - the region proposal stage and the language processing stage.

①Region Proposal Phase

The first phase involves detecting text regions from the image. This is achieved by using a convolutional model that detects text fragments and encloses them in bounding boxes.

The task of the network here is similar to the network that extracts candidate frames in target detection algorithms such as Fast-RCNN, marking and extracting areas of possible interest. These regions are used as attention maps and provided to language processing algorithms along with features extracted from the image.

②Language processing stage

NLP-based network works to extract the information captured in these areas and is built based on the features provided by the CNN layer Meaningful sentences.

Algorithms that directly recognize characters without going through this step (based entirely on CNNs) have been successfully explored in recent work and are particularly useful for detecting text with limited temporal information to be conveyed Useful, such as vehicle license plates.

How to improve the accuracy of OCR

1. Data denoising

On the input The model data is properly denoised. Denoising can be done in a variety of ways, with Gaussian blur being the most popular. Additional white noise can also be removed with the help of an auxiliary autoencoder network.

2. Improve image contrast

Image contrast plays an important role in helping the neural network distinguish text areas from non-text areas. Increasing the contrast difference between text and background helps OCR models perform better.

Optical character recognition application scenarios

1. Document recognition: Document recognition is an important and common use case of OCR, detecting text and identifying .

2. Data entry automation: Use OCR to effectively capture data from documents and forms, automate data entry and reduce data anomalies caused by typing problems.

3. Archives and Digital Library Creation: OCR helps create digital libraries by identifying the categories to which a book or document belongs. These categories can be used to find books in a specific category, helping readers navigate the list seamlessly. Accordingly, OCR helps in digitizing old documents, making preservation extremely easy and safe.

4. Text translation: Text translation is an important part of OCR, especially scene text recognition. Translation modules superimposed on the OCR system output can help understand documents in different languages.

5. Music score recognition: The text detection system can be trained to detect music scores from music scores, allowing the machine to play music directly from text information. This can also be used for listening training.

6. Marketing campaigns: OCR systems have been successfully used in marketing campaigns for fast moving consumer goods by attaching scannable text portions to their products. When scanned via a mobile camera or capture device, this text portion can be converted into a text code in place of a promotional code.

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