What is an OCR pipeline?

Optical Character Recognition (OCR) is used to process images or scanned documents to produce raw text or other structured output. Using OCR software, a company can process all of their scanned loan applications. This pipeline transforms scanned documents into raw text data with OCR.

What does OCR for scanned documents mean?

Optical Character Recognition
OCR stands for “Optical Character Recognition.” It is a technology that recognizes text within a digital image. It is commonly used to recognize text in scanned documents and images. The OCR program which will recognize the text and convert the document to an editable text file.

How do I create an OCR model?

Building your own Attention OCR model

1. Gather annotated training data.
2. Get crops for each frame of each video where the number plates are.
3. Generate tfrecords for all the cropped files.
4. Place them in models/research/attention_ocr/python/datasets as required (in the FSNS dataset format).
5. Train the model using Attention OCR.

How do I scan a document using OCR?

Scan & OCR

1. Select Scan & OCR from the Tools center or right-hand pane.
2. Select a file.
3. Choose Scanned Document or Camera Image to enhance the document.
4. Select Enhance to clean up the image.
5. Select Recognize Text to manually recognize text on image files.

What is OCR in computer vision?

Optical Character Recognition (OCR) — A branch Of Computer Vision. Optical Character Recognition (OCR) is the tool that is used when a scanned document or photo is taken and converted into text.

What is text detection and segmentation in OCR?

One of the most important module in optical character recognition pipeline is the text detection and segmentation which is also called as text localization. In the previous blog, we have seen various techniques to pre-process the input image which can help in improving our OCR accuracy.

What technology did we use to build our OCR pipeline?

We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more. In addition, we will also dive deep into what it took to actually make our OCR pipeline production-ready at Dropbox scale.

Can OCR and object detection be combined?

Combining OCR with Object Detection is highly useful when we need to extract particular pieces of information, but it can still be difficult to determine what is what (determining which piece of text is the answer to which question for example).