How to Improve OCR with NLP

OCR, or optical character recognition, is the process of converting images of text into digital text. Common uses for OCR include digitizing books and documents for archiving or indexing searchable content online. It’s also commonly used in invoicing software, medical records software, and many other business-oriented programs that involve a lot of reading. However, there are plenty of OCR algorithms out there that aren’t great at recognizing characters. To improve your OCR with NLP (Natural Language Processing), you need to train your algorithm to understand what it’s looking at and how to identify it. This article will detail how you can break down your OCR process with NLP to determine which words or phrases give you the most trouble and build a training set that explicitly covers those cases.

What is NLP?

NLP, natural language processing, is a subfield of artificial intelligence that analyzes human language. It could be spoken language or written language, but it’s primarily focused on understanding what language means. As such, it can be used for many applications, ranging from sentiment analysis and text analytics to improving your OCR. NLP is intended to mimic the way humans understand language. Similar to how you can look at a sentence and understand it without breaking it down word-by-word, NLP algorithms use statistical techniques to determine the most probable meaning of a sentence based on its context.

How NLP Helps with OCR

As we’ve established, OCR uses algorithms to convert images of text into digital text. While you might think that the most efficient way to do this is to look at each character individually, algorithms broadly use a “coarse-to-fine” approach. In other words, they start by scanning the image as a whole, identifying broad feature groups (e.g., “lines running vertically”), and then identifying smaller feature groups within those lines based on their characteristics (e.g. “lines with upturned edges”). The algorithm will then attempt to identify the most probable matching characters for each feature it has identified, which is where NLP comes in. Computational linguistics is helpful for OCR algorithms in several ways. First, they can be used to “clean up” the text that you’re scanning by, for example, removing extra spaces or punctuation, or correcting common spelling errors (e.g., “impact” and “impacts”). Second, they can help algorithms understand the context of the sentence. For example, they can help algorithms identify prepositions and proper nouns by the knowledge that “Apple is releasing their new iPhone on October 3rd” differs from “I like apples.”

Identifying Problems in Your OCR Process

Before improving your OCR with NLP, you must identify what needs improvement. There are a few things to look out for when scanning text and digitizing it, including – Accuracy: How many words in the document are being misread? How many characters are being misread? – Consistency: Are your algorithms consistently misreading the exact words or characters? – Speed: How long does it take to scan a document? How long does it take to convert it into digital text? – Usability: Are certain documents that your algorithms aren’t working with?

Improving Your OCR with NLP: Dialogue Vocabulary and Sentence Repetition

If your algorithms are consistently misreading the same words, you can try to train them to look for specific patterns in these words to determine what they are. For example, if your algorithms keep misreading the word “And” (which is one of the most misread words in English), you could train the algorithm to look for a specific pattern of pixels to determine whether a given the word is “And” or not. However, this approach is time-consuming. It’s also limited to patterns found in single words. If you have multiple misread words with similar patterns but different meanings, this approach will have varying degrees of success. A better option is to train your algorithm to recognize larger patterns of words, such as dialogue. For example, “Hello, how are you? I am fine, thank you.” This can be used as a “silent majority rule” where the algorithm doesn’t need to recognize every word in the document, just a few keywords that you want to highlight. This approach also scales well if you have multiple documents with the same dialogue.

Improving your OCR with NLP: Anchors and Shapes Detection

This approach will work better for documents that don’t have a lot of dialogue. Rather than recognizing words, you’ll use anchor points and shape recognition to train your algorithm to look for specific “landmarks” in the document you’re scanning. For example, suppose your algorithms are consistently misreading the numbers “51” and “87” as “5” and “8”. In that case, you could identify the pixel that represents the “1” in “51” and train your algorithm to understand that the “5” is directly below that pixel and the “1” is directly above it. Or, if your algorithms are misreading the number “87” as “8”, you could create an anchor point at the 8 and then have a second anchor point directly below that 8 and represent the 7. This approach scales well if you have multiple documents with similar numbers.

Improving your OCR with NLP: Word Spotting and Character Recognition Strategies

Finally, you can train your algorithms to recognize and distinguish between different types of words by looking at them as a whole rather than in parts. For example, you can train your algorithms to realize that “A” is different from “The” by analyzing the word as a whole, regardless of where the letters are positioned. This is known as word spotting, and while it’s more challenging to implement than the other strategies we’ve talked about, it’s more reliable and less likely to produce false positives. You can also train your algorithm to recognize which words are used most often and prioritize those. Or you can train your algorithm to identify specific character sequences, such as “The End,” regardless of how the letters are positioned. Again, this is most useful for documents that have been scanned for a long time.

Conclusion

OCR is an imperfect process, and you could barely make it 100% accurate. However, you can improve your OCR with NLP by training algorithms to look for specific patterns of words or pixel arrangements. It’s essential to consider which types of documents you’re scanning and which algorithms might work best with them. While algorithms make digitizing text quicker and more efficient, they aren’t perfect. They’ll always make mistakes, but they can be vastly improved with the right training and algorithms. DeepDatum specializes in information retrieval from complex documents. To know how we can help, please visit https://www.deepdatum.ai.