Optical character recognition using tessaract and javascript
PHOENIX (OCR) is an optical chatacter recognition that converts images of text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image. OCR is used in a wide variety of applications, including:
- Document digitization: OCR can be used to digitize old documents, such as books, newspapers, and magazines. This makes it possible to search and edit the documents electronically, and to make them accessible to people with disabilities.
- Data entry: OCR can be used to automate data entry tasks, such as entering customer information or product data into a database. This can save businesses time and money.
- Machine translation: OCR can be used to translate text from one language to another. This is useful for businesses that need to communicate with customers or partners in other countries.
- Medical transcription: OCR can be used to transcribe medical records from paper to electronic format. This can improve the accuracy of medical records and make it easier for doctors and nurses to access them.
OCR systems typically consist of two components: hardware and software. The hardware component is an optical scanner or a specialized circuit board that captures the image of the text. The software component then analyzes the image and converts it into machine-encoded text.
The hierarchy of PHOENIX(OCR) refers to the different levels of abstraction at which OCR systems operate. The different levels are:
- Image Acquisition: This is the first step, and It is the collection of images for conversion into printed format from other sources.
- Preprocessing: This is the second step, and it involves preparing the image of the text for further processing. This may involve tasks such as removing noise, adjusting contrast, and binarizing the image.
- Character Segmentation: This is the third step, and it involves dividing the image into individual characters or words. This is often done using a combination of edge detection and clustering algorithms.
- Feature extraction: This is the fourth step, and it involves extracting features from each character or word. These features may be based on the shape of the characters, the grayscale values of the pixels, or the statistical distribution of the pixels.
- Character classification: This is the fifth step, and it helps the segmented characters to arrange them into different categories and classes.
- Character recognition: This is the sixth step, and it involves identifying the characters or words in the image based on the extracted features. This may be done using a variety of techniques, such as template matching, neural networks, or support vector machines.
- Post-processing: This is the final step, and it involves correcting any errors that were made in the previous levels. This may involve tasks such as removing noise, correcting segmentation errors, and correcting character recognition errors.
The hierarchy of OCR is important because it allows OCR systems to be modular and scalable. Each level of the hierarchy can be implemented using different techniques, and the levels can be combined in different ways to achieve different levels of accuracy and performance.
The hierarchy of OCR is still evolving, and there is ongoing research into new techniques for each level of the hierarchy. As OCR technology continues to develop, it is likely that the hierarchy will become more complex and sophisticated.
OCR is a powerful technology that can be used to automate a variety of tasks. It is used in a wide variety of industries, and it is becoming increasingly common in everyday life.