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Ctcwordbeamsearch

Ctcwordbeamsearch is a C++ CTC word beam-search decoder distributed with a Python package interface. It decodes recognition-model outputs using a corpus-derived dictionary and can optionally apply word-level language-model scoring. Its documented applications include handwritten text recognition and automatic speech recognition.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ctcwordbeamsearch — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it after a recognition model has produced softmax probabilities in the required CTC tensor layout, when dictionary constraints or word-level language-model scoring are useful.

Who it is for

  • Developers adding dictionary-constrained decoding to a CTC-based text-recognition pipeline.
  • Researchers or engineers evaluating word-level language-model scoring for recognition outputs.
  • Python users who want to call a C++ decoder component.

Documented uses

  • Decode CTC model probability outputs into character-label sequences.
  • Constrain candidate words using a corpus-derived dictionary.
  • Optionally score word sequences with a language model.
  • Preserve numbers, punctuation, or other non-word characters between recognized words.
Before you choose

Requirements & limits

The public GitHub repository is licensed under MIT. Its README documents a Python package installation path and lists Python 3.11 and 3.12 support.

  • The input must be a NumPy array shaped time steps × batch × (characters + 1), with softmax already applied and the CTC blank in the final position.
  • The supplied character sequence must match the recognition model’s output-class order.
  • The corpus is used to build the dictionary and, in language-model modes, the language model; word characters determine how words are extracted.
  • The four scoring modes trade off dictionary-only decoding, n-gram scoring, and forecast-based scoring; the forecast-and-sample mode considers at most 20 possible next words.
  • The repository documents both a Python package interface and C++ implementation, plus a TensorFlow custom-operation extra.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Python package and check installation

Install CTCWordBeamSearch from the repository and run its documented installation check.

Before you start

  • The CTCWordBeamSearch repository.
  • Python; the README lists support for Python 3.11 and 3.12.
  • pytest for the documented test command.

Steps

  1. From the repository root, run the command below.

    Documented command
    pip install .

  2. Change to the tests/ directory and run pytest to check whether installation worked.

Expected outcome

The README’s installation check runs the repository tests with pytest.

Guide limitations

  • This procedure checks installation; it does not configure a recognition model or demonstrate decoding.
  • The README does not specify additional environment or platform setup in these steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
githubharald/CTCWordBeamSearch · Official homepage title · retained in its source language
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