Boost Text Recognition with Multi-Step Validation

Aug 05, 2025 · Alok

Accurate text recognition can be tough, especially when the documents are blurry, characters overlap, or backgrounds are noisy. With Dynamsoft Capture Vision (DCV) 3.0, we’ve introduced new enhancements to address these challenges through multi-step recognition and validation workflow, designed to enhance accuracy, especially in complex scanning scenarios like Machine Readable Zones.

What is Dynamsoft Capture Vision?

Dynamsoft Capture Vision (DCV) is a modular SDK that brings together powerful components to support image capturing, content understanding, result parsing, and interactive workflows, all in one cohesive solution.

Through the Image Source Adapter (ISA) interface, DCV enables easy integration with various image sources. Key modules include

Dynamsoft Barcode Reader (DBR) – for reading 1D/2D barcodes
Dynamsoft Document Normalizer (DDN) – for detecting and correcting document boundaries
Dynamsoft Label Recognizer (DLR) – for recognizing structured text
Dynamsoft Code Parser (DCP) – for parsing meaningful fields from text results

Developers can access both intermediate and final results using interfaces like Capture Result Receiver (CRR) and Intermediate Result Receiver (IRR), while Dynamsoft Camera Enhancer (DCE) provides visualization and editing tools.

Developers can access both intermediate and final results using interfaces like Capture Result Receiver (CRR) and Intermediate Result Receiver (IRR), while Dynamsoft Camera Enhancer (DCE) provides visualization and editing tools.

Multi-Step Recognition and Validation

DCV 3.0 employs seven steps to improve text-line accuracy. These steps combine AI models, pattern matching, and linguistic tools to simulate human-live reading and correction:

TextLine Recognition Model

The process starts with a deep learning model that reads the entire line of text in one go. This model uses an end-to-end CRNN (Convolutional Recurrent Neural Network) architecture to generate the first prediction. In this example, the initial recognition result was F2N09 AU6.

textline-recognition

Character Feature Scorer

Next, the system evaluates individual characters based on how clearly they appear. It calculates features scores for each character and flag the ones that seem ambiguous or poorly defined. Here characters “N” and “0” were identified as suspicious, prompting further review in next stages.

character-feature-scorer

Confusable Character Corrector

This component tackles characters that are commonly misread - like “0” vs “O” or “I”, “l” or “1”, using standard font characteristics and visual patterns. This improves the overall accuracy of the word. In this case “0” was corrected to “O”.

confusable-character-corrector

Character Recognition Model

For characters still deemed uncertain after scoring, the system re-evaluates them using a dedicated character-level recognition model. This refined model zeros in on individual glyphs, offering an alternative prediction. Here unclear “N” was corrected to “H”, although the confidence level remained low, requiring further validation.

character-recognition-model

Character Overlapping Matcher

Sometimes characters overlap or break due to print issues or camera movement. This step is trained to detect and resolve such overlaps, reinforcing the result through structural analysis. In this case, it conformed the earlier correction of “N” to “H”, reinforcing the result through structural analysis.

character-overlapping-matcher

Regex Corrector

Regex-based rules help validate the text format. If a known pattern is expected, for example a string starting with the word “P”, this step applies that pattern to adjust the output, accordingly, aligning with the expected format using the regular expression. Here system updated “F” to “P”, aligning with the expected format using regular expression ^P.*

regex-corrector

Dictionary-Based Corrector

The final step compares results against a predefined dictionary of valid words, acronyms, or known values. This ensures that final outputs are contextually correct. For our sample, “AU6” was replaced with “AUG”, a match found in custom dictionary provided.

dictionary-based corrector

Real World Scenarios

This multi-step recognition is especially effective in handling:

Line noise and character adhesion
Complex or textured backgrounds
Blurred or motion-affected captures
Overlapping or broken characters

While currently optimized for Machine Readable Zone (MRZ) use cases, the framework is highly customizable. With targeted training and configuration, it can be adapted for a wide range of applications including shipping and logistics labels, retail price tags, medical device barcodes or vehicle registration numbers.

How It Works

The entire process works on cropped text-line images and outputs a complete text string. There’s no need for intermediate character segmentation, which simplifies processing and improves speed, making it ideal for mobile and real-time applications.

Developers benefit from this enhanced accuracy without additional integration work. The entire validation sequence is embedded into the DLR mode of Dynamsoft Capture Vision and works automatically.

Conclusion

The new multi-step recognition and validation system in DLR combines machine learning, rule-based logic, and dictionary intelligence to deliver results that are more accurate, reliable and robust, even in less-than-ideal conditions.

If you’re developing applications that rely on accurate text recognition from images, DCV 3.0 could be a game changer for you.