Extracting and repurposing data from scanned documents, camera images, and image-only PDFs can be tricky.
However, a technologically advanced ML-based OCR engine can perform the task efficiently.
This article will discuss how an OCR engine works and why OCR SDK could be the right fit for your needs.
What Is Optical Character Recognition (OCR)?
OCR – Optical Character Recognition – converts an image of text into an AI-enabled machine-readable text format.
OCR has tremendous benefits over simple scans because you can’t edit, search, or count the words in the image file using a text editor.
However, OCR can convert the image into a text document, storing its contents as text data.
What Is The Importance Of An OCR Engine?
Today, the majority of business workflows involve receiving information from print media. Invoices, paper forms, scanned legal documents, and printed contracts are part of business processes.
It takes a lot of time and space to store and manage these large volumes of paperwork.
Here, OCR offers paperless document management benefits over manual intervention, which is tedious and slow.
Improved AI-based OCR technology solves the problem by converting text images into text data that can be analyzed by other business software.
The processed data is then incorporated to conduct analytics, streamline operations, and automate processes, ultimately improving productivity.
How Does An OCR Engine Work?
Image Acquisition
Image acquisition is the first step where a scanner reads documents and converts them to binary data. It categorizes the light areas as background and the dark areas as text to analyze the scanned image.
Image Preprocessing
The acquisition process comes with dirt and errors. So, the OCR engine first cleans the image and discards the errors before reading.
These cleaning techniques:
Text Recognition
Pattern matching and feature extraction are the two main types of OCR algorithms processes that OCR software mainly uses for text recognition.
Pattern Matching
The next step is matching the pattern by separating a character image named glyph and comparing it with a similarly stored glyph.
The process works only when the stored glyph has a font and scale similar to the input glyph.
Feature Extraction
The next step is feature extraction. The process breaks down or decomposes the glyphs into features such as lines, closed loops, line direction, and line intersections.
These features find the best match or the nearest neighbor among its various stored glyphs.
Postprocessing
Finally, after analysis, the system converts the extracted text data into a computerized file.
What Are Typical OCR Use Cases?
What Are The Key Ways OCR Engines Help Businesses Today?
How Can You Integrate An OCR SDK?
FileStack’s OCR SDK helps digitize documents and extract and organize data from credit cards, passports, driver’s licenses, and tax receipts without lifting a finger.
OCR from FileStack organizes and streamlines the data capture process, so you don’t have to.
To extract the text inside the complex documents in the images, FileStack has two different machine learning-based solutions that work accurately.
- Unsupervised learning with intelligent image processing
- Supervised segmentation
Advanced document detection and preprocessing tools are FileStack’s latest addition that can increase accuracy.
Firstly, FileStack’s API uploads the images to its databases. Then, transform them into a unified format, and resize them to a standard size.
Afterward, they are fed into document detection and preprocessing tools to make the image clearer for the OCR engine. The results generate a JSON response containing all the information of the extracted texts into the original image.
In the processing API, OCR is available as a synchronous operation. Following this task:
| ocr |
Correspondingly, the response:
| { “document”: { “text_areas”: [ { “bounding_box”: [ { “x”: 834, “y”: 478 }, { “x”: 3372, “y”: 739 }, { “x”: 3251, “y”: 1907 }, { “x”: 714, “y”: 1646 } ], “lines”: [ { “bounding_box”: [ { “x”: 957, “y”: 490 }, { “x”: 3008, “y”: 701 }, { “x”: 2977, “y”: 1009 }, { “x”: 925, “y”: 797 } ], “text”: “Filestack can detect”, “words”: [ { “bounding_box”: [ { “x”: 957, “y”: 490 }, { “x”: 1833, “y”: 580 }, { “x”: 1802, “y”: 888 }, { “x”: 925, “y”: 797 } ], “text”: “Filestack” }, { “bounding_box”: [ { “x”: 1916, “y”: 589 }, { “x”: 2266, “y”: 625 }, { “x”: 2235, “y”: 932 }, { “x”: 1884, “y”: 896 } ], “text”: “can” }, { “bounding_box”: [ { “x”: 2336, “y”: 632 }, { “x”: 3008, “y”: 701 }, { “x”: 2977, “y”: 1009 }, { “x”: 2304, “y”: 939 } ], “text”: “detect” } ] }, { “bounding_box”: [ { “x”: 860, “y”: 858 }, { “x”: 3330, “y”: 1049 }, { “x”: 3301, “y”: 1421 }, { “x”: 831, “y”: 1229 } ], “text”: “printed and handwritten”, “words”: [ { “bounding_box”: [ { “x”: 860, “y”: 858 }, { “x”: 1550, “y”: 912 }, { “x”: 1521, “y”: 1283 }, { “x”: 831, “y”: 1229 } ], “text”: “printed” }, { “bounding_box”: [ { “x”: 1677, “y”: 922 }, { “x”: 2047, “y”: 951 }, { “x”: 2018, “y”: 1321 }, { “x”: 1648, “y”: 1292 } ], “text”: “and” }, { “bounding_box”: [ { “x”: 2107, “y”: 954 }, { “x”: 3330, “y”: 1049 }, { “x”: 3301, “y”: 1421 }, { “x”: 2078, “y”: 1326 } ], “text”: “handwritten” } ] }, { “bounding_box”: [ { “x”: 749, “y”: 1305 }, { “x”: 2504, “y”: 1486 }, { “x”: 2469, “y”: 1826 }, { “x”: 714, “y”: 1645 } ], “text”: “texts using OCR”, “words”: [ { “bounding_box”: [ { “x”: 749, “y”: 1305 }, { “x”: 1233, “y”: 1355 }, { “x”: 1198, “y”: 1695 }, { “x”: 714, “y”: 1645 } ], “text”: “texts” }, { “bounding_box”: [ { “x”: 1317, “y”: 1364 }, { “x”: 1910, “y”: 1425 }, { “x”: 1875, “y”: 1765 }, { “x”: 1282, “y”: 1704 } ], “text”: “using” }, { “bounding_box”: [ { “x”: 1972, “y”: 1431 }, { “x”: 2504, “y”: 1486 }, { “x”: 2469, “y”: 1826 }, { “x”: 1937, “y”: 1771 } ], “text”: “OCR” } ] } ], “text”: “Filestack can detect\nprinted and handwritten\ntexts using OCR” } ] }, “text”: “Filestack can detect\nprinted and handwritten\ntexts using OCR\n”, “text_area_percentage”: 23.40692449819434 } |
Depending on the response parameters, you can get the OCR response on your image like the following:
| https://cdn.filestackcontent.com/security=p:<POLICY>,s:<SIGNATURE>/ocr/<HANDLE> |
You can use OCR in a chain with other tasks such as doc_detection:
| https://cdn.filestackcontent.com/security=p:<POLICY>,s:<SIGNATURE>/doc_detection=coords:false,preprocess:true/ocr/<HANDLE> |
Also, use OCR with an external URL:
| https://cdn.filestackcontent.com/<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/ocr/<EXTERNAL_URL> |
Finally, use OCR with Storage Aliases:
| https://cdn.filestackcontent.com/<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/ocr/src://<STORAGE_ALIAS>/<PATH_TO_FILE> |
